Book contents
- Frontmatter
- Contents
- Credits and Acknowledgments
- Introduction
- 1 Distributed Constraint Satisfaction
- 2 Distributed Optimization
- 3 Introduction to Noncooperative Game Theory: Games in Normal Form
- 4 Computing Solution Concepts of Normal-Form Games
- 5 Games with Sequential Actions: Reasoning and Computing with the Extensive Form
- 6 Richer Representations: Beyond the Normal and Extensive Forms
- 7 Learning and Teaching
- 8 Communication
- 9 Aggregating Preferences: Social Choice
- 10 Protocols for Strategic Agents: Mechanism Design
- 11 Protocols for Multiagent Resource Allocation: Auctions
- 12 Teams of Selfish Agents: An Introduction to Coalitional Game Theory
- 13 Logics of Knowledge and Belief
- 14 Beyond Belief: Probability, Dynamics, and Intention
- Appendices: Technical Background
- Bibliography
- Index
7 - Learning and Teaching
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Credits and Acknowledgments
- Introduction
- 1 Distributed Constraint Satisfaction
- 2 Distributed Optimization
- 3 Introduction to Noncooperative Game Theory: Games in Normal Form
- 4 Computing Solution Concepts of Normal-Form Games
- 5 Games with Sequential Actions: Reasoning and Computing with the Extensive Form
- 6 Richer Representations: Beyond the Normal and Extensive Forms
- 7 Learning and Teaching
- 8 Communication
- 9 Aggregating Preferences: Social Choice
- 10 Protocols for Strategic Agents: Mechanism Design
- 11 Protocols for Multiagent Resource Allocation: Auctions
- 12 Teams of Selfish Agents: An Introduction to Coalitional Game Theory
- 13 Logics of Knowledge and Belief
- 14 Beyond Belief: Probability, Dynamics, and Intention
- Appendices: Technical Background
- Bibliography
- Index
Summary
The capacity to learn is a key facet of intelligent behavior, and it is no surprise that much attention has been devoted to the subject in the various disciplines that study intelligence and rationality. We will concentrate on techniques drawn primarily from two such disciplines—artificial intelligence and game theory—although those in turn borrow from a variety of disciplines, including control theory, statistics, psychology and biology, to name a few. We start with an informal discussion of the various subtle aspects of learning in multiagent systems and then discuss representative theories in this area.
Why the subject of “learning” is complex
The subject matter of this chapter is fraught with subtleties, and so we begin with an informal discussion of the area. We address three issues—the interaction between learning and teaching, the settings in which learning takes place and what constitutes learning in those settings, and the yardsticks by which to measure this or that theory of learning in multiagent systems.
The interaction between learning and teaching
Most work in artificial intelligence concerns the learning performed by an individual agent. In that setting the goal is to design an agent that learns to function successfully in an environment that is unknown and potentially also changes as the agent is learning. A broad range of techniques have been developed, and learning rules have become quite sophisticated.
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- Multiagent SystemsAlgorithmic, Game-Theoretic, and Logical Foundations, pp. 189 - 222Publisher: Cambridge University PressPrint publication year: 2008
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