COMP90054 AI Planning for Autonomy The University of Melbourne School of Computing and Information Systems Project 2, 2019 Contest: Pacman Capture the Flag Deadline: 23:59 Wednesday 16 October 2019 This project counts towards 40% of the marks for this subject. This is an team project-assignment and has to be done in groups of 3 (or […]
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
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# search.py # ——— # Licensing Information: You are free to use or extend these projects for # educational purposes provided that (1) you do not distribute or publish # solutions, (2) you retain this notice, and (3) you provide clear # attribution to UC Berkeley, including a link to http://ai.berkeley.edu. # # Attribution Information:
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Lecture 13. Multi-armed bandits COMP90051 Statistical Machine Learning Semester 2, 2019 Lecturer: Ben Rubinstein Copyright: University of Melbourne COMP90051 Statistical Machine Learning This lecture • Stochasticmulti-armedbandits ∗ Where we learn to take actions; we receive only indirect supervision in the form of rewards; and we only observe rewards for actions taken – the simplest setting
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COMP90051 Statistical Machine Learning Project 2 Description Due date: 4:00pm Thursday, 17th October 2019 Weight: 25%1 Multi-armed bandits (MABs) are a powerful tool in statistical machine learning: they bridge decision making, control, optimisation and learning; they address practical problems of sequential decision making while backed by elegant theoretical guarantees; they are relatively easily implemented, efficient
class: “GraphSearchTest” algorithm: “uniformCostSearch” diagram: “”” C ^ | 2 2 V 4 *A B —–> [H] |1 1.5 V 2.5 G E | 2 | V [F] A is the start state, F and H is the goal. Arrows mark possible state transitions. The number next to the arrow is the cost of that
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class: “PacmanSearchTest” algorithm: “uniformCostSearch” points: “0.5” # The following specifies the layout to be used layoutName: “testSearch” layout: “”” %%%%% %.P % %%% % %. % %%%%% “”” searchProblemClass: “FoodSearchProblem” leewayFactor: “2”
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class: “GraphSearchTest” algorithm: “uniformCostSearch” diagram: “”” 1 1 1 *A —> B —> C —> [G] | ^ | 10 | \———————/ A is the start state, G is the goal. Arrows mark possible state transitions. The number next to the arrow is the cost of that transition. If you fail this test case, you
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Lecture 18. Gaussian Mixture Model. Expectation Maximization. COMP90051 Statistical Machine Learning Semester 2, 2019 Lecturer: Ben Rubinstein Copyright: University of Melbourne COMP90051 Statistical Machine Learning This lecture • Unsupervisedlearning ∗ Diversity of problems • Gaussianmixturemodel(GMM) ∗ A probabilistic approach to clustering ∗ The GMM model ∗ GMM clustering as an optimisation problem • TheExpectationMaximization(EM)algorithm 2
# Graph where natural action choice leads to an infinite loop class: “GraphSearchTest” algorithm: “uniformCostSearch” diagram: “”” B C ^ /| | / | V / V *A
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