Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Introduction to Artificial Intelligence – Unit 10 Communication Course 67842 The Hebrew University of Jerusalem School of Engineering and Computer Science Academic Year: 2008/2009 Instructor: Jeff Rosenschein (Chapter 22, “Artificial Intelligence: A Modern Approach”) Real Language Real human languages provide many problems for NLP: ambiguity anaphora indexicality vagueness discourse structure metonymy metaphor noncompositionality 2 Ambiguity Beach closing down last year Squad helps dog bite victim Helicopter powered by human flies American pushes bottle up Germans I ate spaghetti with meatballs with salad with abandon with a fork with a friend Ambiguity can be lexical (polysemy), syntactic, semantic, referential 3 Ambiguity resolved in speech The sentence: “I never said she stole my money.” can have seven different meanings depending on which word is stressed I never said she stole my money. I never said she stole my money. I never said she stole my money. I never said she stole my money. I never said she stole my money. I never said she stole my money. I never said she stole my money. 4 Anaphora Using pronouns to refer back to entities already introduced in the text: After Mary proposed to John, they found a preacher and got married. For the honeymoon, they went to Hawaii. Mary saw a ring through the window and asked John for it. Mary threw a rock at the window and broke it. 5 Indexicality Indexical sentences refer to utterance situation (place, time, S/H, etc.): I am over here. Why did you do that? 6 Metonymy Using one noun phrase to stand for another: I’ve read Shakespeare. Chrysler announced record profits. The ham sandwich on Table 4 wants another beer. 7 Metaphor “Non-literal” usage of words and phrases, often systematic: I’ve tried killing the process but it won’t die. Its parent keeps it alive. 8 Noncompositionality basketball shoes baby shoes alligator shoes designer shoes brake shoes red book red pen red hair red herring small moon large molecule mere child alleged murderer real leather artificial grass 9 What this Course Covered 10 Overall Structure Intelligent Agents, What is AI? Search Knowledge Representation Planning Probabilistic Reasoning Game Theory Natural Language/Learning 11 More Detailed Structure 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 12 More Detailed Structure 2 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 13 AI: A Dynamic Field 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… 14 What are the State-of-the-Art Research Topics? IJCAI’09 meets 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 15 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 16 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 17 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 18 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 & GameTheoretic KR Learning Relational and Graphical Models 19 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 20 What are the AI Apps to Come? 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? 21 AI (and Software) Ethical Issues 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? 22