COSC1127/1125 Artificial Intelligence School of Computing Technologies Semester 2, 2021 Prof. ̃a Tutorial No. 5 KR&R II – First Order Logic For some of the questions below, you may want to check the great slides by (CSC 384 at University of Toronto) on Skolemization, Most General Unifiers, First-Order Resolution. It includes the Skolem- ization process […]
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COSC1125/1127: Artificial Intelligence Week 9: Reinforcement Learning Instructor: Prof. RMIT University, Melbourne, Australia [These slides were created by and for CS188 Intro to AI at UC Berkeley. All CS188 materials are available at http://ai.berkeley.edu.] Please retain proper attribution, including the reference to ai.berkeley.edu. Thanks! ‹#› Week 9: From MDP to Reinforcement Learning Some news… Take
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Knowledge Representation II FOL Resolution & Prolog [AIMA 4G] Chapters 8 & 9 COSC1127/1125 Artificial Intelligence Semester 2, 2021 Prof. * The slides here are those from and KR book and ‘s one for Thinking as Computation book (see inside). — Wominjeka! Welcome! I acknowledge, and invite you all to acknowledge, the Traditional Owners (Wurundjeri
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COSC1127/1125 Artificial Intelligence School of Computing Technologies Semester 2, 2021 Prof. ̃a Tutorial No. 6 Automated Planning PART1: Conceptual……………………………………………………………………… Explain informally but clearly and precisely. (a) Closed world assumption. (b) The frame problem. Provide two examples. (c) The three components an action using the STRIPS language? Explain, in your own words, each each of the
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COSC1127/1125 Artificial Intelligence School of Computing Technologies Semester 2, 2021 Prof. ̃a Tutorial No. 8 Markov Decision Processes (MDPs) You can check this video on max/min vs arg max/arg min. PART 1: MDPs . . . . . . . . . . . . . . . . . . . . . .
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COSC1127/1125 Artificial Intelligence School of Computing Technologies Semester 2, 2021 Prof. ̃a Tutorial No. 10 Bayesian Networks PART1: Conditionalprobabiltygraphs……………………………………………………. We can use the full joint probability tables to answer any question we may have about a set of random variables; however, as the number of variables increases, the size of the joint probability table increases
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Bayesian Networks [AIMA 4G] Chapter 13.1-13.3, 13.5 [13.2.3, 13.2.4, 13.4] Artificial Intelligence COSC1127/1125 Semester 2, 2021 Prof. * Many slides are based on those and , and some based on ‘s ones. Thanks to all them! Wominjeka! Week 10 Bayesian Networks Prof. What are the chances that there was an earthquake given that the alarm
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Adversarial Search [AIMA G4] Chapter 6 Sections 6.1-6.3 (6.4, 15.1-15.3) COSC1127/1125 Artificial Intelligence Semester 2, 2021 Prof. * Some slides are based on slides from and those from Fahiem Bacchus. — Wominjeka! Welcome! I acknowledge, and invite you all to acknowledge, the Traditional Owners (Wurundjeri people of the Kulin Nations) of the land on which
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COSC1127/1125 Artificial Intelligence School of Computing Technologies Semester 2, 2021 Prof. ̃a Tutorial No. 9 Reinforcement Learning You can check this video on max/min vs arg max/arg min; and this video on the formulas for Temporal Difference in the AIMIA book. PART 1: Passive agents . . . . . . . . . .
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COSC1127/1125 Artificial Intelligence School of Computing Technologies Semester 2, 2021 Prof. ̃a Tutorial No. 3 Adversarial Search The XKCD of the week… (thanks Andrew!) PART1: Minimaxsearch…………………………………………………………………. Consider the following game tree. Player MAX plays first and is represented with rectangles; MIN player is represented with circles. Numbers in each node are names used for convenience
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