AI Planning for Autonomy
AI Planning for Autonomy
(COMP90054)
Graduate coursework / Points: 12.5 / Dual-Delivery (Parkville)
In 2021, there will be three delivery modes for your subjects – Dual-Delivery, Online and On Campus. Please refer to the return to campus page (https://students.unimelb.edu.au/student-support/coronavirus/return-to-campus/subjects) for more information on these delivery modes and students who can enrol in each mode based on their location.
Overview
Availability Semester 1 – Dual-Delivery Semester 2 – Dual-Delivery
AIMS
The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. 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). The subject focuses on the foundations that enable agents to reason autonomously about
Fees
Look up fees
(https://students.unimelb.edu.au/your-course/manage-your- course/fees-and-payments/understanding-your-fees)
goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability.
This site uses and shares cookies and similar technologies to personalise your exTpherpierongcream, amdinvgerlatinsgeuatogeyuosuedanindthpisrosuvbidjeectcios nPtyethnotnf.rNoomletchtuirdes-poar rwtoierkssahospws eolnl as
Python will be delivered.
analyse our usage. You consent to our use of such technologies by proceeding. You can change your mind or consent choices at any time. Visit our Privacy
INDICATIVE CONTENT
Statement for further information.
ar
Topics are drawn from the field of advanced
Accept cookies
Search algorithms and heuristic function
s
tificial intelligence including:
Cookie Preferences
Classical (AI) planning Markov Decision Processes Reinforcement learning Game theory
Ethics in AI planning
Intended learning outcomes
On completion of this subject the student is expected to:
Apply theoretical concepts of reasoning about actions to single and multi-agent problems
Be able to analyse, design, and implement automated planning, reinforcement learning, and game theoretic techniques to given problems
Understand the strengths, weaknesses, and ethical consequences of different approaches for reasoning about action
Be able to critically evaluate and choose the right technique for different problems in reasoning about action
Communicate technical solutions about automated planning, reinforcement learning, and game theory
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
ThLiasstituepudsaetes da:n1d Jsuhnaere2s0c2o1okies and similar technologies to personalise your experience, advertise to you and provide content from third-parties as well as analyse our usage. You consent to our use of such technologies by proceeding. You can change your mind or consent choices at any time. Visit our Privacy Statement for further information.
Accept cookies Cookie Preferences