'Terminal states' diaporamas de présentation

Decision-Making with Probabilistic Uncertainty

Decision-Making with Probabilistic Uncertainty. Administrative. HW4 deadline moved to Thursday 10/29. Last Time. Nondeterministic uncertainty In action: action -> set of possible future states In sensing: belief state = set of possible current states

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CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Carla P. Gomes gomes@cs.cornell.edu Module: Adversarial Search (Reading R&N: Chapter 6). Outline. Game Playing Optimal decisions Minimax ?-? pruning Imperfect, real-time decisions. Game Playing. Mathematical Game Theory

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LTL Model Checking

LTL Model Checking. Wishnu Prasetya wishnu@cs.uu.nl www.cs.uu.nl/docs/vakken/pv. Overview. This pack : Abstract model of programs Temporal properties Verification (via model checking) algorithm Concurrency . Finite State Automaton/Machine. Abstraction of a real program Choices

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General Framework

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Game Playing

Game Playing. "Abstract" games are of interest to AI (artificial intelligence) because: Games states are accessible and easy to represent Games are usually restricted to a small number of well-defined actions Successful programs which play complex games are evidence of machine intelligence

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269202 Algorithms for iSNE

269202 Algorithms for iSNE. Dr. Kenneth Cosh Week 12. Review. Searching Uninformed Searches Breadth First Depth First Iterative Informed Searches Best First Heuristics Hill Climbing Simulated Annealing. This Week. Adversarial Searches

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Adversarial Search & Game Playing

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AI in game (IV)

AI in game (IV). Oct. 11, 2006. So far. Artificial Intelligence: A Modern Approach Stuart Russell and Peter Norvig Prentice Hall, 2 nd ed. Chapter 1: AI taxonomy Chapter 2: agents Chapter 3: uninformed search Chapter 4: informed search. From now on.

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I.Ya. HORBACHEVSKY TERNOPIL STATE MEDICAL UNIVERSITY Disaster medicine and military medicine department

I.Ya. HORBACHEVSKY TERNOPIL STATE MEDICAL UNIVERSITY Disaster medicine and military medicine department. Lecture 2 Cardiac, pulmonary and cerebral reanimation at pre-hospital stage .

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Introduction to Hierarchical Reinforcement Learning

Introduction to Hierarchical Reinforcement Learning. Jervis Pinto. Slides adapted from Ron Parr ( From ICML 2005 Rich Representations for Reinforcement Learning Workshop ) and Tom Dietterich (From ICML99). Contents. Purpose of HRL Design issues

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Game Playing and Game Programming (Chapter 6)

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Adversarial Search

Adversarial Search. Chapter 6. Outline. Optimal decisions ?-? pruning Imperfect, real-time decisions. Games as search problems. Engaged intellectual faculties of humans. Board games such as Chess. Game playing is appealing target of AI. State of the game is easy to represent . And

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CS 188: Artificial Intelligence Spring 2007

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Checking Interaction Consistency in MARMOT Component Refinements

Checking Interaction Consistency in MARMOT Component Refinements Yunja Choi School of Electrical Engineering and Computer Science Kyungpook National University Overview MARMOT methodology Component and refinements Interaction consistency A general framework for consistency checking

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Adversarial Search

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Reinforcement Learning

Reinforcement Learning. Variation on Supervised Learning Exact target outputs are not given Some variation of reward is given either immediately or after some steps Chess Path Discovery

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For Monday

For Monday. Read chapter 7, sections 1-4 Homework: Chapter 4, exercise 1 Chapter 5, exercise 9. Program 1. Any questions?. Making Imperfect Decisions. Generating the complete game tree is intractable for most games Alternative: Cut off search

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