Download Introduction to Artificial Intelligence – Course 67842

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:
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ambiguity
anaphora
indexicality
vagueness
discourse structure
metonymy
metaphor
noncompositionality
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Ambiguity
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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
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with salad
with abandon
with a fork
with a friend
 Ambiguity can be lexical (polysemy), syntactic,
semantic, referential
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Ambiguity resolved in speech
 The sentence:
“I never said she stole my money.”
can have seven different meanings
depending on which word is stressed
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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.
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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.
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Indexicality
 Indexical sentences refer to utterance
situation (place, time, S/H, etc.):
 I am over here.
 Why did you do that?
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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.
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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.
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Noncompositionality
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basketball shoes
baby shoes
alligator shoes
designer shoes
brake shoes
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red book
red pen
red hair
red herring
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small moon
large molecule
mere child
alleged murderer
real leather
artificial grass
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What this Course Covered
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Overall Structure
 Intelligent Agents, What is AI?
 Search
 Knowledge Representation
 Planning
 Probabilistic Reasoning
 Game Theory
 Natural Language/Learning
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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
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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
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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…
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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
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 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
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 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
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 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
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 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
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 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
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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?
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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?
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