The Hebrew University of Jerusalem School of Engineering and Computer Science Academic Year: 2011/2012 Instructor: Jeff Rosenschein

 Intelligent Agents, What is AI?  Search  Knowledge Representation  Planning  Learning  Game Theory 2

 Introduction: what is AI? the Turing Test; History of AI; state of the art  Intelligent Agents: rationality, environments, agent structure  Search: breadth-first, depth-first, iterative deepening, bidirectional search, informed heuristic search, A*, heuristic functions, hill climbing, simulated annealing, Constraint satisfaction problems, backtracking search for CSPs, Adversarial search, games, minimax, alpha-beta pruning 3

 Knowledge Representation: propositional logic; propositional inference, first-order logic; quantifiers; encoding of knowledge, inference in first-order logic, unification, forward chaining, backward chaining, resolution  Planning: planning with state-space search, partial order planning, planning graphs, planning with propositional logic, hierarchical task network planning, conditional planning, continuous planning, multiagent planning 4

 There are many ways of categorizing approaches to problems in AI ◦ Neat vs. Scruffy  Theoreticians vs. Experimentalists ◦ Rule-based vs. data-based ◦ Users of particular “tools” or “approaches”  POMDPs  Learning ◦ And more… 5

 IJCAI’09 met in Pasadena, July 2009 ◦ Session topics  Cognitive and Philosophical Foundations  Performance and Behavior Modeling in Games  Depth and Breadth First Search  Time Series/Activity Recognition  Diagnosis and Testing  Automated Reasoning  Unsupervised Learning I  Social Choice I: Manipulation  Search in Games 6

 Plan Recognition  Ontology Matching and Learning  Spatial Reasoning  Semi-Supervised Learning I  Multimodal Interaction  Online Games  Distributed Constraint Satisfaction  Model-Based Diagnosis and Applications  Causality and Graphical Models  Transfer Learning  Word Sense Disambiguation  Recommender Systems  Satisfiability I: Extensions and Applications  Multiagent Planning and Learning  Robotics: Multirobot Planning 7

 Preferences: Learning I  Search and Learning  Multiagent Resource Allocation  Argumentation I  Epistemic Logic  Semi-Supervised Learning II: Applications  HTN Planning  Coalitional Games  Unsupervised Learning II  Heuristic Search  Constraints I: Global Constraints  Logic Programming I  Mechanism Design  Reasoning about Action I  Clustering 8

 Text Summarization & Understanding  Preferences: Learning II  Local and Anytime Search  Game Theory: Solution Concepts  Social Choice II: Voting  Constraints II  Optimal Planning  Description Logics I: Reasoning  Metric Learning  POMDPs II  Morphology and Counting  Vision & Robotics I: Novelty  Preferences: Graphical Models  Planning: Search Techniques  Vision & Robotics II 9

 Social Choice III  Advances in A* Search  Contingent and Nondeterministic Planning  Activity and Goal Recognition  Reasoning about Action II  Parsing and Translation  Coalitions and Coordination  Learning: Dimensionality Reduction  Inference in Graphical Models  Games and Monte Carlo Search  Web Mining and Web Services  Negotiation and Commitment  Spatio-Temporal Reasoning/Distributed & Game- Theoretic KR  Learning Relational and Graphical Models 10

 Kernel Methods  Natural Language Semantics  Musical Expression/Vision & Robotics III  Constraints III  Logic Programming II  Description Logics II: Query Answering  Auctions  Structure Learning  Markov Decision Processes  Satisfiability II  Description Logics III: Non-standard Reasoning  Argumentation II  Social Networks  Learning: Matrix Factorization  Reinforcement Learning 11

 Long-held dreams are coming true: ◦ Language Translation ◦ Speech Recognition  Mundane tasks made possible by learning from data: ◦ FareCast  What would we want a machine to do, that it can’t do now? ◦ Autonomous Driving? ◦ Teaching? 12

 When computers are programmed to take the place of humans, where does liability reside?  Is fast behavior unethical, when slow versions of the same behavior are ethical (e.g., machine scanning of vast amounts of mortgage information, publically available, that would be much harder to analyze if done by a human)?  Human-machine symbiosis – what crosses the line?  Machine-machine behavior – is any behavior that is unethical for humans allowed for computers? Vice versa? 13

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