Projects - User contributions [en]

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T08:18:18Z

A1660069: /* Interface System Design */

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

=Introduction=

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

=Background=

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

=Motivation=

Street Engine is a new developed computer architecture which is developed by The University of Adelaide. In previous years, two groups has been proved Street Engine is able to achieve advanced cognitive computation in real-time, such as Tic Tac Toe group. But the Artificial General Intelligence agent cannot be limited in one or two single function achievement.

A suitable experimental system for Artificial General Intelligent Street Agents remains as a challenge to a the Street project research group. Therefore, Our group expect to build a virtual and physical platform with embedded Street Agent to achieve multi-agent with multi-functions to reproduce simple cognitive behaviours of insects, such as ants foraging behaviours.

=Technical Background=

'''Street'''

[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

'''V-rep simulation'''

Virtual robot experimentation platform was used in our project. It is involved to test our Street Agent logical and test our project design. The remote control interface is used to communicate designed Street Agent with virtual rovers.

'''Arduino Uno R3 with WIFI'''

Physical robot platform was built by using Arduino board with necessary sensors. Arduino board and 3D printed rover structure. Arduino rover is used to collect the environment information and receive the movement command which is from Street agent, such as avoiding boundary, approaching food source, approaching nest location and following pheromone trail.

=Interface System Design=

[[File:111.PNG]]

All system need to be controlled via own deigned Street agent logical. Interface System consists of processing module and action module, processing module is used to
process all data from outside of the system, action module is used to process all commands from Street Agent decision and send to action module to enable rovers achieve the specified functions.

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T08:14:15Z

A1660069: /* Interface System Design */

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

=Introduction=

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

=Background=

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

=Motivation=

Street Engine is a new developed computer architecture which is developed by The University of Adelaide. In previous years, two groups has been proved Street Engine is able to achieve advanced cognitive computation in real-time, such as Tic Tac Toe group. But the Artificial General Intelligence agent cannot be limited in one or two single function achievement.

A suitable experimental system for Artificial General Intelligent Street Agents remains as a challenge to a the Street project research group. Therefore, Our group expect to build a virtual and physical platform with embedded Street Agent to achieve multi-agent with multi-functions to reproduce simple cognitive behaviours of insects, such as ants foraging behaviours.

=Technical Background=

'''Street'''

[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

'''V-rep simulation'''

Virtual robot experimentation platform was used in our project. It is involved to test our Street Agent logical and test our project design. The remote control interface is used to communicate designed Street Agent with virtual rovers.

'''Arduino Uno R3 with WIFI'''

Physical robot platform was built by using Arduino board with necessary sensors. Arduino board and 3D printed rover structure. Arduino rover is used to collect the environment information and receive the movement command which is from Street agent, such as avoiding boundary, approaching food source, approaching nest location and following pheromone trail.

=Interface System Design=

[[File:111.PNG]]

All system need to be controlled via own deigned Street agent logical. Interface System consists of processing module and action module, processing module is used to

all data from outside of the system need to be sent to our own designed processing module.

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T08:05:54Z

A1660069: /* Interface System Design */

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

=Introduction=

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

=Background=

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

=Motivation=

Street Engine is a new developed computer architecture which is developed by The University of Adelaide. In previous years, two groups has been proved Street Engine is able to achieve advanced cognitive computation in real-time, such as Tic Tac Toe group. But the Artificial General Intelligence agent cannot be limited in one or two single function achievement.

A suitable experimental system for Artificial General Intelligent Street Agents remains as a challenge to a the Street project research group. Therefore, Our group expect to build a virtual and physical platform with embedded Street Agent to achieve multi-agent with multi-functions to reproduce simple cognitive behaviours of insects, such as ants foraging behaviours.

=Technical Background=

'''Street'''

[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

'''V-rep simulation'''

Virtual robot experimentation platform was used in our project. It is involved to test our Street Agent logical and test our project design. The remote control interface is used to communicate designed Street Agent with virtual rovers.

'''Arduino Uno R3 with WIFI'''

Physical robot platform was built by using Arduino board with necessary sensors. Arduino board and 3D printed rover structure. Arduino rover is used to collect the environment information and receive the movement command which is from Street agent, such as avoiding boundary, approaching food source, approaching nest location and following pheromone trail.

=Interface System Design=

[[File:111.PNG]]

All system need to be controlled via own deigned Street agent logical. For all information from environment need to be translate and send to the agent, agent will send the decision back to platform. The flexible processing module and action module have been established for ensure the interaction of agent and platform.

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T08:05:19Z

A1660069: /* Interface System Design */

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

=Introduction=

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

=Background=

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

=Motivation=

Street Engine is a new developed computer architecture which is developed by The University of Adelaide. In previous years, two groups has been proved Street Engine is able to achieve advanced cognitive computation in real-time, such as Tic Tac Toe group. But the Artificial General Intelligence agent cannot be limited in one or two single function achievement.

A suitable experimental system for Artificial General Intelligent Street Agents remains as a challenge to a the Street project research group. Therefore, Our group expect to build a virtual and physical platform with embedded Street Agent to achieve multi-agent with multi-functions to reproduce simple cognitive behaviours of insects, such as ants foraging behaviours.

=Technical Background=

'''Street'''

[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

'''V-rep simulation'''

Virtual robot experimentation platform was used in our project. It is involved to test our Street Agent logical and test our project design. The remote control interface is used to communicate designed Street Agent with virtual rovers.

'''Arduino Uno R3 with WIFI'''

Physical robot platform was built by using Arduino board with necessary sensors. Arduino board and 3D printed rover structure. Arduino rover is used to collect the environment information and receive the movement command which is from Street agent, such as avoiding boundary, approaching food source, approaching nest location and following pheromone trail.

=Interface System Design=

[[File:picture.PNG]]

All system need to be controlled via own deigned Street agent logical. For all information from environment need to be translate and send to the agent, agent will send the decision back to platform. The flexible processing module and action module have been established for ensure the interaction of agent and platform.

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T08:04:21Z

A1660069: /* Interface System Design */

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

=Introduction=

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

=Background=

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

=Motivation=

Street Engine is a new developed computer architecture which is developed by The University of Adelaide. In previous years, two groups has been proved Street Engine is able to achieve advanced cognitive computation in real-time, such as Tic Tac Toe group. But the Artificial General Intelligence agent cannot be limited in one or two single function achievement.

A suitable experimental system for Artificial General Intelligent Street Agents remains as a challenge to a the Street project research group. Therefore, Our group expect to build a virtual and physical platform with embedded Street Agent to achieve multi-agent with multi-functions to reproduce simple cognitive behaviours of insects, such as ants foraging behaviours.

=Technical Background=

'''Street'''

[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

'''V-rep simulation'''

Virtual robot experimentation platform was used in our project. It is involved to test our Street Agent logical and test our project design. The remote control interface is used to communicate designed Street Agent with virtual rovers.

'''Arduino Uno R3 with WIFI'''

Physical robot platform was built by using Arduino board with necessary sensors. Arduino board and 3D printed rover structure. Arduino rover is used to collect the environment information and receive the movement command which is from Street agent, such as avoiding boundary, approaching food source, approaching nest location and following pheromone trail.

=Interface System Design=

All system need to be controlled via own deigned Street agent logical. For all information from environment need to be translate and send to the agent, agent will send the decision back to platform. The flexible processing module and action module have been established for ensure the interaction of agent and platform.

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T08:02:58Z

A1660069: /* Interface System Design */

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

=Introduction=

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

=Background=

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

=Motivation=

Street Engine is a new developed computer architecture which is developed by The University of Adelaide. In previous years, two groups has been proved Street Engine is able to achieve advanced cognitive computation in real-time, such as Tic Tac Toe group. But the Artificial General Intelligence agent cannot be limited in one or two single function achievement.

A suitable experimental system for Artificial General Intelligent Street Agents remains as a challenge to a the Street project research group. Therefore, Our group expect to build a virtual and physical platform with embedded Street Agent to achieve multi-agent with multi-functions to reproduce simple cognitive behaviours of insects, such as ants foraging behaviours.

=Technical Background=

'''Street'''

[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

'''V-rep simulation'''

Virtual robot experimentation platform was used in our project. It is involved to test our Street Agent logical and test our project design. The remote control interface is used to communicate designed Street Agent with virtual rovers.

'''Arduino Uno R3 with WIFI'''

Physical robot platform was built by using Arduino board with necessary sensors. Arduino board and 3D printed rover structure. Arduino rover is used to collect the environment information and receive the movement command which is from Street agent, such as avoiding boundary, approaching food source, approaching nest location and following pheromone trail.

=Interface System Design=

All system need to be controlled via own deigned Street agent logical. For all information from environment need to be translate and send to the agent, agent will send the decision back to platform. The flexible processing module and action module have been established for ensure the interaction of agent and platform.
[[File:picture.PNG]]

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T07:59:55Z

A1660069: /* Interface System Design */

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

=Introduction=

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

=Background=

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

=Motivation=

Street Engine is a new developed computer architecture which is developed by The University of Adelaide. In previous years, two groups has been proved Street Engine is able to achieve advanced cognitive computation in real-time, such as Tic Tac Toe group. But the Artificial General Intelligence agent cannot be limited in one or two single function achievement.

A suitable experimental system for Artificial General Intelligent Street Agents remains as a challenge to a the Street project research group. Therefore, Our group expect to build a virtual and physical platform with embedded Street Agent to achieve multi-agent with multi-functions to reproduce simple cognitive behaviours of insects, such as ants foraging behaviours.

=Technical Background=

'''Street'''

[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

'''V-rep simulation'''

Virtual robot experimentation platform was used in our project. It is involved to test our Street Agent logical and test our project design. The remote control interface is used to communicate designed Street Agent with virtual rovers.

'''Arduino Uno R3 with WIFI'''

Physical robot platform was built by using Arduino board with necessary sensors. Arduino board and 3D printed rover structure. Arduino rover is used to collect the environment information and receive the movement command which is from Street agent, such as avoiding boundary, approaching food source, approaching nest location and following pheromone trail.

=Interface System Design=

All system need to be controlled via own deigned Street agent logical. For all information from environment need to be translate and send to the agent, agent will send the decision back to platform. The flexible processing module and action module have been established for ensure the interaction of agent and platform.

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T07:59:09Z

A1660069: /* Interface System Design */

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

=Introduction=

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

=Background=

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

=Motivation=

Street Engine is a new developed computer architecture which is developed by The University of Adelaide. In previous years, two groups has been proved Street Engine is able to achieve advanced cognitive computation in real-time, such as Tic Tac Toe group. But the Artificial General Intelligence agent cannot be limited in one or two single function achievement.

A suitable experimental system for Artificial General Intelligent Street Agents remains as a challenge to a the Street project research group. Therefore, Our group expect to build a virtual and physical platform with embedded Street Agent to achieve multi-agent with multi-functions to reproduce simple cognitive behaviours of insects, such as ants foraging behaviours.

=Technical Background=

'''Street'''

[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

'''V-rep simulation'''

Virtual robot experimentation platform was used in our project. It is involved to test our Street Agent logical and test our project design. The remote control interface is used to communicate designed Street Agent with virtual rovers.

'''Arduino Uno R3 with WIFI'''

Physical robot platform was built by using Arduino board with necessary sensors. Arduino board and 3D printed rover structure. Arduino rover is used to collect the environment information and receive the movement command which is from Street agent, such as avoiding boundary, approaching food source, approaching nest location and following pheromone trail.

=Interface System Design=

All system need to be controlled via own deigned Street agent logical. For all information from environment need to be translate and send to the agent, agent will send the decision back to platform. The flexible processing module and action module have been established for ensure the interaction of agent and platform.
[[File:picture.PNG]]

File:Picture.png

2018-10-21T07:58:34Z

A1660069: A1660069 uploaded a new version of "File:Picture.png"

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T06:42:02Z

A1660069: /* Interface System Design */

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

=Introduction=

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

=Background=

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

=Motivation=

Street Engine is a new developed computer architecture which is developed by The University of Adelaide. In previous years, two groups has been proved Street Engine is able to achieve advanced cognitive computation in real-time, such as Tic Tac Toe group. But the Artificial General Intelligence agent cannot be limited in one or two single function achievement.

A suitable experimental system for Artificial General Intelligent Street Agents remains as a challenge to a the Street project research group. Therefore, Our group expect to build a virtual and physical platform with embedded Street Agent to achieve multi-agent with multi-functions to reproduce simple cognitive behaviours of insects, such as ants foraging behaviours.

=Technical Background=

'''Street'''

[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

'''V-rep simulation'''

Virtual robot experimentation platform was used in our project. It is involved to test our Street Agent logical and test our project design. The remote control interface is used to communicate designed Street Agent with virtual rovers.

'''Arduino Uno R3 with WIFI'''

Physical robot platform was built by using Arduino board with necessary sensors. Arduino board and 3D printed rover structure. Arduino rover is used to collect the environment information and receive the movement command which is from Street agent, such as avoiding boundary, approaching food source, approaching nest location and following pheromone trail.

=Interface System Design=
[File:pciture.PNG]]
All system need to be controlled via own deigned Street agent logical. For all information from environment need to be translate and send to the agent, agent will send the decision back to platform. The flexible processing module and action module have been established for ensure the interaction of agent and platform.
[[File:picture.PNG]]

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T06:40:47Z

A1660069: /* Technical Background */

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

=Introduction=

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

=Background=

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

=Motivation=

Street Engine is a new developed computer architecture which is developed by The University of Adelaide. In previous years, two groups has been proved Street Engine is able to achieve advanced cognitive computation in real-time, such as Tic Tac Toe group. But the Artificial General Intelligence agent cannot be limited in one or two single function achievement.

A suitable experimental system for Artificial General Intelligent Street Agents remains as a challenge to a the Street project research group. Therefore, Our group expect to build a virtual and physical platform with embedded Street Agent to achieve multi-agent with multi-functions to reproduce simple cognitive behaviours of insects, such as ants foraging behaviours.

=Technical Background=

'''Street'''

[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

'''V-rep simulation'''

Virtual robot experimentation platform was used in our project. It is involved to test our Street Agent logical and test our project design. The remote control interface is used to communicate designed Street Agent with virtual rovers.

'''Arduino Uno R3 with WIFI'''

Physical robot platform was built by using Arduino board with necessary sensors. Arduino board and 3D printed rover structure. Arduino rover is used to collect the environment information and receive the movement command which is from Street agent, such as avoiding boundary, approaching food source, approaching nest location and following pheromone trail.

=Interface System Design=

All system need to be controlled via own deigned Street agent logical. For all information from environment need to be translate and send to the agent, agent will send the decision back to platform. The flexible processing module and action module have been established for ensure the interaction of agent and platform.
[[File:picture.PNG]]

File:Picture.png

2018-10-21T06:38:22Z

A1660069:

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T06:37:31Z

A1660069:

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

=Introduction=

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

=Background=

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

=Motivation=

Street Engine is a new developed computer architecture which is developed by The University of Adelaide. In previous years, two groups has been proved Street Engine is able to achieve advanced cognitive computation in real-time, such as Tic Tac Toe group. But the Artificial General Intelligence agent cannot be limited in one or two single function achievement.

A suitable experimental system for Artificial General Intelligent Street Agents remains as a challenge to a the Street project research group. Therefore, Our group expect to build a virtual and physical platform with embedded Street Agent to achieve multi-agent with multi-functions to reproduce simple cognitive behaviours of insects, such as ants foraging behaviours.

=Technical Background=

'''Street'''

[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

'''V-rep simulation'''

Virtual robot experimentation platform was used in our project. It is involved to test our Street Agent logical and test our project design. The remote control interface is used to communicate designed Street Agent with virtual rovers.

'''Arduino Uno R3 with WIFI'''

Physical robot platform was built by using Arduino board with necessary sensors. Arduino board and 3D printed rover structure. Arduino rover is used to collect the environment information and receive the movement command which is from Street agent, such as avoiding boundary, approaching food source, approaching nest location and following pheromone trail.

=Interface System Design=

All system need to be controlled via own deigned Street agent logical. For all information from environment need to be translate and send to the agent, agent will send the decision back to platform. The flexible processing module and action module have been established for ensure the interaction of agent and platform.
[[File:picture.PNG]]

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T06:20:19Z

A1660069: /* Technical Background */

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

=Introduction=

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

=Background=

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

=Motivation=

Street Engine is a new developed computer architecture which is developed by The University of Adelaide. In previous years, two groups has been proved Street Engine is able to achieve advanced cognitive computation in real-time, such as Tic Tac Toe group. But the Artificial General Intelligence agent cannot be limited in one or two single function achievement.

A suitable experimental system for Artificial General Intelligent Street Agents remains as a challenge to a the Street project research group. Therefore, Our group expect to build a virtual and physical platform with embedded Street Agent to achieve multi-agent with multi-functions to reproduce simple cognitive behaviours of insects, such as ants foraging behaviours.

=Technical Background=

'''Street'''
[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

=Technical Background=

'''Street'''

[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T06:19:45Z

A1660069:

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

=Introduction=

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

=Background=

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

=Motivation=

Street Engine is a new developed computer architecture which is developed by The University of Adelaide. In previous years, two groups has been proved Street Engine is able to achieve advanced cognitive computation in real-time, such as Tic Tac Toe group. But the Artificial General Intelligence agent cannot be limited in one or two single function achievement.

A suitable experimental system for Artificial General Intelligent Street Agents remains as a challenge to a the Street project research group. Therefore, Our group expect to build a virtual and physical platform with embedded Street Agent to achieve multi-agent with multi-functions to reproduce simple cognitive behaviours of insects, such as ants foraging behaviours.

=Technical Background=

'''Street'''

[[File:pciture.PNG]]

=Technical Background=

'''Street'''

[[File:pciture.PNG]]

As shown in figure above, street includes Street Engine and Street language. The street language is used on the street engine directly. Therefore, the street processing has reduced the latency and power consumption. Street engine uses global memory to operate every single processing element.
Street Engine is core system of the Street Agent, The Street Language is parallel structure which means the system does not force the system following flow control, every single command in system is independent. comparing current architectures, the real time processing efficiency has been improved.

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T05:37:30Z

A1660069: /* Technical Background */

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T05:37:14Z

A1660069: /* Technical Background */

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T05:36:40Z

A1660069: /* Technical Background */

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T05:35:20Z

A1660069: /* Technical Background */

File:Pciture.PNG

2018-10-21T05:34:08Z

A1660069:

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T05:33:46Z

A1660069:

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T05:13:10Z

A1660069:

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

'''Introduction'''

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

'''Background'''

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

'''Motivation'''

A suitable experimental system for Street Agents

New computer architecture has been developed in 2015 at The University of Adelaide. A suitable experimental system for Street Agents remains as a challenge to the Street project team. Therefore, our aim is to develop a artificial intelligent system, including multi-functions, based on Street, with virtual and physical UGV platforms for cognitive AI agents.It is expected that Street Agent is able to support our platform to achieve the artificial general intelligence functions.

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-21T04:47:27Z

A1660069:

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

'''Introduction'''

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

'''Background'''

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

'''Motivation'''

New computer architecture has been developed in 2015 at The University of Adelaide. A suitable experimental system for Street Agents remains as a challenge to the Street project team. Therefore, our aim is to develop a artificial intelligent system, including multi-functions, based on Street, with virtual and physical UGV platforms for cognitive AI agents.It is expected that Street Agent is able to support our platform to achieve the artificial general intelligence functions.

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-19T08:47:18Z

A1660069:

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

'''Introduction'''

This project aims to use Street Agent to develop an suitable experimental platform which is able to reproduce the Ant's foraging behaviours. This project has developed an entire ant foraging logical by using Street language and an multi-agent interface system for Street Agent. Own designed platform has been proved the Street Engine can be used to reproduce simple cognitive behaviours of ants.

'''Background'''

In biology, ants walk randomly and leave a trail of pheromone when they foraging, once an ant find food, ant can follow pheromone trail back to the nest, other ants will follow this pheromone to approach food
when they find this pheromone trail.
Street Cognitive Architecture is first developed based on Soar Cognitive Architecture at The University of Adelaide. It is aimed be used for artificial intelligent(AGI) applications. The street language has developed for building street engine, which is based on pattern-matching rules.
In 2018, honour project team at the university has developed own designed street agent logical and an multi-agent entire suitable experimental platform using street agent to achieve ants cooperation foraging behaviours, including recognize food, nest location and pheromone. During project period, the street language has been used to create an artificial general intelligent system.

The projects aims to reproduce the simple behaviors of ants that finding food and carrying food back to nest. We will be using the Street language to build the AI agent. the AI agent will be test in visual environment simulation. Furthermore a physical platform will be build to support AI agent. A robot's behaviors includes reaching food, depositing food, carrying food and laying pheromone.

Projects:2018s1-160 UAV Platform for Cognitive AI Agent

2018-10-19T08:19:41Z

A1660069:

'''Students'''

Junyi Jiang

Zhi Cao

'''Supervisors'''

Prof. Michael Liebelt

Mr. Xin Yuan

'''Introduction'''

This project aims to develop a UGV p

The projects aims to reproduce the simple behaviors of ants that finding food and carrying food back to nest. We will be using the Street language to build the AI agent. the AI agent will be test in visual environment simulation. Furthermore a physical platform will be build to support AI agent. A robot's behaviors includes reaching food, depositing food, carrying food and laying pheromone.