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

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.