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  3. AI Planning for Autonomy

AI Planning for Autonomy (COMP90054)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 2
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AIMS

The key focus of this subject is the foundations of automated planning and reasoning and their real-world applications. Automated planning is the AI approach to developing agents that make their own decisions and is becoming increasingly popular. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). This subject shows how this work is relevant for many applications beyond the traditional area of artificial intelligence, such as resource scheduling, logistics, process management, service composition, intelligent sensing and robotics. The subject focuses on the foundations that enable agents to reason autonomously about goals, perception, actions and the knowledge of other agents during collaborative task execution.

This subject is an elective subject in the Master of Science (Computer Science) and Master of Information Technology, in particular for the Distributed Computing and the Computing Specialisations. It can also be taken as an Advanced Elective subject in the Master of Engineering (Software).

INDICATIVE CONTENT

Topics are drawn from the field of advanced artificial intelligence including:

  • Search algorithms and heuristic functions
  • Classical (AI) planning
  • Probabilistic planning
  • Non-deterministic planning
  • Reinforcement learning
  • Multi-agent and epistemic planning
  • Game theory
  • Ethics in AI planning

Intended learning outcomes

INTENDED LEARNING OUTCOMES (ILO)

On completion of this subject the student is expected to:

  1. Understand the theoretical concepts of AI planning and reasoning techniques
  2. Be able to apply AI planning and reasoning techniques to analyse, design and implement non-trivial distributed problems
  3. Be able to evaluate and select suitable AI planning and reasoning techniques to problems
  4. Understand the strengths and weaknesses of different AI planning and reasoning approaches
  5. Describe the ethical implications of AI planning and reasoning to individuals and society

INTENDED LEARNING OUTCOMES (ILO)


On completion of this subject the student is expected to:






  • Understand the theoretical concepts of automated planning and reasoning techniques
  • Be able to apply agent modelling techniques to analyse, design and implement a small agent-based system
  • Be able to evaluate, design, and implement automated planning and reasoning techniques
  • Understand the strengths and weaknesses of different automated planning and reasoning approaches for software agents
  • Be able to apply automated planning and concurrent programming techniques to non-trivial distributed problems.

Generic skills

On completion of the subject the students should have the following skills:

  • Ability to undertake problem identification, formulation, and solution
  • Ability to utilise a systems approach to complex problems and to design and operational performance
  • Ability to manage information and documentation
  • Capacity for creativity and innovation Ability to communicate effectively with both the engineering team and the community at large.

Last updated: 3 November 2022