COMP3702 Artificial Intelligence Semester 2, 2021 Tutorial 8 Monte Carlo tree search This material is adapted from: C. B. Browne et al., “A Survey of Monte Carlo Tree Search Methods,” in IEEE Transactions on Computational Intelligence and AI in Games, vol. 4, no. 1, pp. 1-43, March 2012, http: // www. diego-perez. net/ papers/ MCTSSurvey. […]
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COMP3702 Artificial Intelligence – Module 3: Reasoning and planning under uncertainty — Part 2 MDPs (Offline methods) COMP3702 Artificial Intelligence Module 3: Reasoning and planning under uncertainty — Part 2 MDPs (Offline methods) Dr Alina Bialkowski Semester 2, 2021 The University of Queensland School of Information Technology and Electrical Engineering Week 7: Logistics • RiPPLE
class: “PacmanSearchTest” algorithm: “uniformCostSearch” points: “0.5” # The following specifies the layout to be used layoutName: “mediumMaze” layout: “”” %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % P% % %%%%%%%%%%%%%%%%%%%%%%% %%%%%%%% % % %% % % %%%%%%% %% % % %% % % % % %%%% %%%%%%%%% %% %%%%% % %% % % % % %% %% % % %% %
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# searchTestClasses.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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class: “GraphSearchTest” algorithm: “uniformCostSearch” diagram: “”” B ^ | *A –> C –> G | V D A is the start state, G is the goal. Arrows mark possible state transitions. This tests whether you extract the sequence of actions correctly even if your search backtracks. If you fail this, your nodes are not correctly
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Tutorial Questions | Week 7 COSC2779 – Deep Learning This tutorial is aimed at reviewing practical methodology in developing CNN. Please try the questions before you join the session. The question below has similar structure to the case study questions that you can expect in the end semester test. 1. You have been hired by
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rubric-assignment1 RMIT Classification: Trusted# Weight Elements HD+ HD DI CR PA PA- NN A p p ro ac h 50% 1) Data exploration leading to well informed approach. 2) Identifying an adequate evaluation framework that is tailored to the problem. 3) Well justified network architecture and objective. 4) Hyper parameters selection strategy. 5) Approach satisfies
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# Graph where BFS finds the optimal solution but DFS does not class: “GraphSearchTest” algorithm: “uniformCostSearch” diagram: “”” /– B | ^ | | | *A –>[G] | | ^ | V | \–>D —-/ A is the start state, G is the goal. Arrows mark possible transitions “”” # The following section specifies the
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Deep Learning – COSC2779 – Neural Network Optimization Deep Learning – COSC2779 Neural Network Optimization Dr. Ruwan Tennakoon August 2, 2021 Reference: Chapter 7,8: Ian Goodfellow et. al., “Deep Learning”, MIT Press, 2016. Lecture 3 (Part 1) Deep Learning – COSC2779 August 2, 2021 1 / 56 Outline Part 1: Optimization Techniques 1 Loss Function
COMP3141 – Static Assurance with Types Static Assurance Phantom Types GADTs Type Families Software System Design and Implementation Static Assurance with Types Christine Rizkallah UNSW Sydney Term 2 2021 1 Static Assurance Phantom Types GADTs Type Families Methods of Assurance Static DynamicHybrid Testing assert() Monitors, watchdogs Types Proofs Static Analysers Model Checkers Contracts Gradual Types
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