AI Bibliography
AI Bibliography |
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Parsons, S. D., Gymtrasiewicz, P., & Wooldridge, M. (2012). Game theory and decision theory in agent-based systems. Springer US. |
| Resource type: Book ID no. (ISBN etc.): 9781461511076 BibTeX citation key: Parsons2012 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Computer Science, Data Sciences, Decision Theory, General, Mathematics Subcategories: AI transfer learning, Autonomous systems, Big data, Decision making, Deep learning, Game theory, Machine intelligence, Machine learning, Markov models, Neural nets, Q-learning, Simulations Creators: Gymtrasiewicz, Parsons, Wooldridge Publisher: Springer US Collection: |
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Game Theory And Decision Theory In Agent-Based Systems is a collection of papers from international leading researchers, that offers a broad view of the many ways game theory and decision theory can be applied in agent-based systems, from standard applications of the core elements of the theory to more cutting edge developments. The range of topics discussed in this book provide the reader with the first comprehensive volume that reflects both the depth and breadth of work in applying techniques from game theory and decision theory to design agent-based systems. Chapters include: Selecting Partners; Evolution of Agents with Moral Sentiments in an IPD Exercise; Dynamic Desires; Emotions and Personality; Decision-Theoretic Approach to Game Theory; Shopbot Economics; Finding the Best Way to Join in; Shopbots and Pricebots in Electronic Service Markets; Polynomial Time Mechanisms; Multi-Agent Q-learning and Regression Trees; Satisficing Equilibria; Investigating Commitment Flexibility in Multi-agent Contracts; Pricing in Agent Economies using Multi-agent Q-learning; Using Hypergames to Increase Planned Payoff and Reduce Risk; Bilateral Negotiation with Incomplete and Uncertain Information; Robust Combinatorial Auction Protocol against False-name Bids.
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