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  • 1
    Online Resource
    Online Resource
    San Rafael, California : Morgan & Claypool | Cham : Springer International Publishing | Cham : Imprint: Springer
    UID:
    (DE-627)1657252469
    Format: 1 Online-Ressource (1 PDF (xiii, 172 pages)) , illustrations.
    ISBN: 9783031021190 , 9781681731636
    Series Statement: Synthesis lectures on games and computational intelligence # 2
    Content: 2. The first place where trouble arose -- 2.1 Representation: what is it? -- 2.1.1 Finite state machines, direct representation -- 2.1.2 Finite state machines with a cellular representation -- 2.1.3 Boolean formulas -- 2.1.4 Function stacks -- 2.1.5 ISAc lists -- 2.1.6 Markov chains and look-up tables -- 2.1.7 Artificial neural nets -- 2.2 Fingerprinting: a tool for comparing across representations -- 2.2.1 Definition of fingerprints -- 2.2.2 Example fingerprint computation -- 2.2.3 Fingerprint results -- 2.3 The uncontrolled variable discovery -- 2.3.1 Experimental design -- 2.3.2 Experimental results -- 2.3.3 Conclusion --
    Content: 3. Problems beyond representation -- 3.1 Changing the payoff matrix changes the agents that arise -- 3.1.1 Design of experiments -- 3.1.2 Results and discussion -- 3.1.3 Conclusions -- 3.2 Changing the level of resources -- 3.2.1 Design of experiments -- 3.2.2 Results and discussion -- 3.2.3 Conclusions and next steps -- 3.3 Algorithm details matter to population size, elite fraction, and mutation rate -- 3.3.1 Design of experiments -- 3.3.2 Results and discussion -- 3.3.3 Conclusions and next steps -- 3.4 Including tags and geography -- 3.4.1 Grid-based nIPD and agent specifications -- 3.4.2 Experimental design -- 3.4.3 Results -- 3.4.4 Conclusions --
    Content: 4. Does all this happen outside of prisoner's dilemma? -- 4.1 Coordination prisoner's dilemma, rock-paper-scissors, and morphs -- 4.1.1 Design of experiments -- 4.1.2 Results and discussion -- 4.1.3 Conclusions -- 4.2 Divide-the-dollar--a more complex game -- 4.2.1 Generalize divide-the-dollar -- 4.2.2 Design of experiments -- 4.2.3 Results and discussion -- 4.2.4 Conclusions and next steps -- 4.3 The snowdrift game -- 4.3.1 The game models -- 4.3.2 Experimental results -- 4.3.3 Discussion -- 4.3.4 Conclusions --
    Content: 5. Noise! -- 5.1 Noisy games are different -- 5.1.1 Experimental design -- 5.1.2 Analysis techniques -- 5.1.3 Results and discussion -- 5.1.4 Conclusions and next steps -- 5.2 Evolutionary velocity -- 5.2.1 Definition of evolutionary velocity -- 5.2.2 Analysis of evolutionary velocity --
    Content: 6. Describing and designing representations -- 6.1 Does the representation have internal states? -- 6.2 Does the representation use external state information? -- 6.3 Can the representation learn? -- 6.4 Does the representation use random numbers? -- 6.5 Complex representations --
    Content: Bibliography -- Authors' biographies
    Content: Evolving agents to play games is a promising technology. It can provide entertaining opponents for games like Chess or Checkers, matched to a human opponent as an alternative to the perfect and unbeatable opponents embodied by current artificial intelligences. Evolved agents also permit us to explore the strategy space of mathematical games like Prisoner's Dilemma and Rock-Paper-Scissors. This book summarizes, explores, and extends recent work showing that there are many unsuspected factors that must be controlled in order to create a plausible or useful set of agents for modeling cooperation and conflict, deal making, or other social behaviors. The book also provides a proposal for an agent training protocol that is intended as a step toward being able to train humaniform agents - in other words, agents that plausibly model human behavior
    Note: Part of: Synthesis digital library of engineering and computer science. - Includes bibliographical references (pages 161-170). - Compendex. INSPEC. Google scholar. Google book search. - Title from PDF title page (viewed on August 23, 2017) , 1. Introduction -- 1.1 Prisoner's dilemma and other games -- 1.2 Digital evolution: evolving game players -- 1.2.1 Optimization vs. co-evolution -- 1.3 From checkers through the rise of the go machines -- 1.4 Why are the simple games so hard? --
    Additional Edition: 9783031009914
    Additional Edition: 9783031032479
    Additional Edition: 9781681731629
    Additional Edition: Erscheint auch als Druck-Ausgabe 9783031009914
    Additional Edition: Erscheint auch als Druck-Ausgabe 9783031032479
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Online Resource
    Online Resource
    [San Rafael, California] : Morgan & Claypool
    UID:
    (DE-602)gbv_1003065821
    Format: 1 Online-Ressource (xiii, 172 Seiten) , Illustrationen
    ISBN: 1681731630 , 9781681731636
    Series Statement: Synthesis lectures on games and computational intelligence #2
    Content: Evolving agents to play games is a promising technology. It can provide entertaining opponents for games like Chess or Checkers, matched to a human opponent as an alternative to the perfect and unbeatable opponents embodied by current artificial intelligences. Evolved agents also permit us to explore the strategy space of mathematical games like Prisoner's Dilemma and Rock-Paper-Scissors. This book summarizes, explores, and extends recent work showing that there are many unsuspected factors that must be controlled in order to create a plausible or useful set of agents for modeling cooperation and conflict, deal making, or other social behaviors. The book also provides a proposal for an agent training protocol that is intended as a step toward being able to train humaniform agents - in other words, agents that plausibly model human behavior
    Content: 1. Introduction -- 1.1 Prisoner's dilemma and other games -- 1.2 Digital evolution: evolving game players -- 1.2.1 Optimization vs. co-evolution -- 1.3 From checkers through the rise of the go machines -- 1.4 Why are the simple games so hard? --
    Content: 2. The first place where trouble arose -- 2.1 Representation: what is it? -- 2.1.1 Finite state machines, direct representation -- 2.1.2 Finite state machines with a cellular representation -- 2.1.3 Boolean formulas -- 2.1.4 Function stacks -- 2.1.5 ISAc lists -- 2.1.6 Markov chains and look-up tables -- 2.1.7 Artificial neural nets -- 2.2 Fingerprinting: a tool for comparing across representations -- 2.2.1 Definition of fingerprints -- 2.2.2 Example fingerprint computation -- 2.2.3 Fingerprint results -- 2.3 The uncontrolled variable discovery -- 2.3.1 Experimental design -- 2.3.2 Experimental results -- 2.3.3 Conclusion --
    Content: 3. Problems beyond representation -- 3.1 Changing the payoff matrix changes the agents that arise -- 3.1.1 Design of experiments -- 3.1.2 Results and discussion -- 3.1.3 Conclusions -- 3.2 Changing the level of resources -- 3.2.1 Design of experiments -- 3.2.2 Results and discussion -- 3.2.3 Conclusions and next steps -- 3.3 Algorithm details matter to population size, elite fraction, and mutation rate -- 3.3.1 Design of experiments -- 3.3.2 Results and discussion -- 3.3.3 Conclusions and next steps -- 3.4 Including tags and geography -- 3.4.1 Grid-based nIPD and agent specifications -- 3.4.2 Experimental design -- 3.4.3 Results -- 3.4.4 Conclusions --
    Content: 4. Does all this happen outside of prisoner's dilemma? -- 4.1 Coordination prisoner's dilemma, rock-paper-scissors, and morphs -- 4.1.1 Design of experiments -- 4.1.2 Results and discussion -- 4.1.3 Conclusions -- 4.2 Divide-the-dollar--a more complex game -- 4.2.1 Generalize divide-the-dollar -- 4.2.2 Design of experiments -- 4.2.3 Results and discussion -- 4.2.4 Conclusions and next steps -- 4.3 The snowdrift game -- 4.3.1 The game models -- 4.3.2 Experimental results -- 4.3.3 Discussion -- 4.3.4 Conclusions --
    Content: 5. Noise! -- 5.1 Noisy games are different -- 5.1.1 Experimental design -- 5.1.2 Analysis techniques -- 5.1.3 Results and discussion -- 5.1.4 Conclusions and next steps -- 5.2 Evolutionary velocity -- 5.2.1 Definition of evolutionary velocity -- 5.2.2 Analysis of evolutionary velocity --
    Content: 6. Describing and designing representations -- 6.1 Does the representation have internal states? -- 6.2 Does the representation use external state information? -- 6.3 Can the representation learn? -- 6.4 Does the representation use random numbers? -- 6.5 Complex representations --
    Content: Bibliography -- Authors' biographies
    Note: Includes bibliographical references (pages 161-170)
    Additional Edition: ISBN 9781681731629
    Additional Edition: Print version ISBN 9781681731629
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Online Resource
    Online Resource
    San Rafael, California : Morgan & Claypool Publishers | [San Rafael, California] : Morgan & Claypool Publishers
    UID:
    (DE-603)417610424
    Format: 1 Online-Ressource (xiii, 172 Seiten)
    ISBN: 9781681731636
    Series Statement: Synthesis lectures on games and computational intelligence No.2
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 4
    Online Resource
    Online Resource
    Cham : Springer International Publishing | Cham : Springer
    UID:
    (DE-604)BV048594790
    Format: 1 Online-Ressource (1 Online-Ressource (XIV, 172 Seiten))
    Edition: 1st ed. 2017
    ISBN: 9783031021190
    Series Statement: Synthesis Lectures on Games and Computational Intelligence
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-00991-4
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-03247-9
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
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  • 5
    Book
    Book
    [San Rafael, California] : Morgan & Claypool
    UID:
    (DE-604)BV045096379
    Format: xiii, 172 Seiten , Illustrationen, Diagramme
    ISBN: 9781681731629
    Series Statement: Synthesis lectures on games and computational intelligence 2
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-1-68173-163-6
    Language: English
    Subjects: Computer Science
    RVK:
    RVK:
    Keywords: Computerspiel ; Computersimulation ; Agent
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    Online Resource
    Online Resource
    Cham : Springer International Publishing
    UID:
    (DE-605)HT021681824
    Format: 1 Online-Ressource (XIV, 172 p)
    Edition: 1st ed. 2017
    ISBN: 9783031021190
    Series Statement: Synthesis Lectures on Games and Computational Intelligence
    Additional Edition: Printed edition 9783031009914
    Additional Edition: Printed edition 9783031032479
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 7
    Online Resource
    Online Resource
    [San Rafael, California] : Morgan & Claypool
    UID:
    (DE-627)1003065821
    Format: 1 Online-Ressource (xiii, 172 Seiten) , Illustrationen
    ISBN: 1681731630 , 9781681731636
    Series Statement: Synthesis lectures on games and computational intelligence #2
    Content: Evolving agents to play games is a promising technology. It can provide entertaining opponents for games like Chess or Checkers, matched to a human opponent as an alternative to the perfect and unbeatable opponents embodied by current artificial intelligences. Evolved agents also permit us to explore the strategy space of mathematical games like Prisoner's Dilemma and Rock-Paper-Scissors. This book summarizes, explores, and extends recent work showing that there are many unsuspected factors that must be controlled in order to create a plausible or useful set of agents for modeling cooperation and conflict, deal making, or other social behaviors. The book also provides a proposal for an agent training protocol that is intended as a step toward being able to train humaniform agents - in other words, agents that plausibly model human behavior
    Content: 1. Introduction -- 1.1 Prisoner's dilemma and other games -- 1.2 Digital evolution: evolving game players -- 1.2.1 Optimization vs. co-evolution -- 1.3 From checkers through the rise of the go machines -- 1.4 Why are the simple games so hard? --
    Content: 2. The first place where trouble arose -- 2.1 Representation: what is it? -- 2.1.1 Finite state machines, direct representation -- 2.1.2 Finite state machines with a cellular representation -- 2.1.3 Boolean formulas -- 2.1.4 Function stacks -- 2.1.5 ISAc lists -- 2.1.6 Markov chains and look-up tables -- 2.1.7 Artificial neural nets -- 2.2 Fingerprinting: a tool for comparing across representations -- 2.2.1 Definition of fingerprints -- 2.2.2 Example fingerprint computation -- 2.2.3 Fingerprint results -- 2.3 The uncontrolled variable discovery -- 2.3.1 Experimental design -- 2.3.2 Experimental results -- 2.3.3 Conclusion --
    Content: 3. Problems beyond representation -- 3.1 Changing the payoff matrix changes the agents that arise -- 3.1.1 Design of experiments -- 3.1.2 Results and discussion -- 3.1.3 Conclusions -- 3.2 Changing the level of resources -- 3.2.1 Design of experiments -- 3.2.2 Results and discussion -- 3.2.3 Conclusions and next steps -- 3.3 Algorithm details matter to population size, elite fraction, and mutation rate -- 3.3.1 Design of experiments -- 3.3.2 Results and discussion -- 3.3.3 Conclusions and next steps -- 3.4 Including tags and geography -- 3.4.1 Grid-based nIPD and agent specifications -- 3.4.2 Experimental design -- 3.4.3 Results -- 3.4.4 Conclusions --
    Content: 4. Does all this happen outside of prisoner's dilemma? -- 4.1 Coordination prisoner's dilemma, rock-paper-scissors, and morphs -- 4.1.1 Design of experiments -- 4.1.2 Results and discussion -- 4.1.3 Conclusions -- 4.2 Divide-the-dollar--a more complex game -- 4.2.1 Generalize divide-the-dollar -- 4.2.2 Design of experiments -- 4.2.3 Results and discussion -- 4.2.4 Conclusions and next steps -- 4.3 The snowdrift game -- 4.3.1 The game models -- 4.3.2 Experimental results -- 4.3.3 Discussion -- 4.3.4 Conclusions --
    Content: 5. Noise! -- 5.1 Noisy games are different -- 5.1.1 Experimental design -- 5.1.2 Analysis techniques -- 5.1.3 Results and discussion -- 5.1.4 Conclusions and next steps -- 5.2 Evolutionary velocity -- 5.2.1 Definition of evolutionary velocity -- 5.2.2 Analysis of evolutionary velocity --
    Content: 6. Describing and designing representations -- 6.1 Does the representation have internal states? -- 6.2 Does the representation use external state information? -- 6.3 Can the representation learn? -- 6.4 Does the representation use random numbers? -- 6.5 Complex representations --
    Content: Bibliography -- Authors' biographies
    Note: Includes bibliographical references (pages 161-170)
    Additional Edition: 9781681731629
    Additional Edition: Print version 9781681731629
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 8
    Online Resource
    Online Resource
    [San Rafael, California] : Morgan & Claypool
    UID:
    (DE-605)HT020191879
    Format: 1 Online-Ressource (xiii, 172 Seiten) , Illustrationen, Diagramme
    ISBN: 9781681731636
    Series Statement: Synthesis lectures on games and computational intelligence 2
    Content: 2. The first place where trouble arose -- 2.1 Representation: what is it? -- 2.1.1 Finite state machines, direct representation -- 2.1.2 Finite state machines with a cellular representation -- 2.1.3 Boolean formulas -- 2.1.4 Function stacks -- 2.1.5 ISAc lists -- 2.1.6 Markov chains and look-up tables -- 2.1.7 Artificial neural nets -- 2.2 Fingerprinting: a tool for comparing across representations -- 2.2.1 Definition of fingerprints -- 2.2.2 Example fingerprint computation -- 2.2.3 Fingerprint results -- 2.3 The uncontrolled variable discovery -- 2.3.1 Experimental design -- 2.3.2 Experimental results -- 2.3.3 Conclusion --
    Content: 3. Problems beyond representation -- 3.1 Changing the payoff matrix changes the agents that arise -- 3.1.1 Design of experiments -- 3.1.2 Results and discussion -- 3.1.3 Conclusions -- 3.2 Changing the level of resources -- 3.2.1 Design of experiments -- 3.2.2 Results and discussion -- 3.2.3 Conclusions and next steps -- 3.3 Algorithm details matter to population size, elite fraction, and mutation rate -- 3.3.1 Design of experiments -- 3.3.2 Results and discussion -- 3.3.3 Conclusions and next steps -- 3.4 Including tags and geography -- 3.4.1 Grid-based nIPD and agent specifications -- 3.4.2 Experimental design -- 3.4.3 Results -- 3.4.4 Conclusions --
    Content: 4. Does all this happen outside of prisoner's dilemma? -- 4.1 Coordination prisoner's dilemma, rock-paper-scissors, and morphs -- 4.1.1 Design of experiments -- 4.1.2 Results and discussion -- 4.1.3 Conclusions -- 4.2 Divide-the-dollar--a more complex game -- 4.2.1 Generalize divide-the-dollar -- 4.2.2 Design of experiments -- 4.2.3 Results and discussion -- 4.2.4 Conclusions and next steps -- 4.3 The snowdrift game -- 4.3.1 The game models -- 4.3.2 Experimental results -- 4.3.3 Discussion -- 4.3.4 Conclusions --
    Additional Edition: Erscheint auch als Druck-Ausgabe 9781681731629
    Language: English
    Keywords: Electronic books
    Library Location Call Number Volume/Issue/Year Availability
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  • 9
    Online Resource
    Online Resource
    San Rafael : Morgan & Claypool Publishers
    UID:
    (DE-627)897069579
    Format: 1 Online-Ressource (188 pages)
    ISBN: 9781681731636
    Series Statement: Synthesis Lectures on Games and Computational Intelligence Ser
    Content: Intro -- Preface -- Acknowledgments -- Introduction -- Prisoner's Dilemma and Other Games -- Digital Evolution: Evolving Game Players -- Optimization vs. Co-evolution -- From Checkers through the Rise of the Go Machines -- Why are the Simple Games so Hard? -- The First Place Where Trouble Arose -- Representation-What Is It? -- Finite State Machines, Direct Representation -- Finite State Machines with a Cellular Representation -- Boolean Fomulas -- Function Stacks -- ISAc Lists -- Markov Chains and Look-up Tables -- Artificial Neural Nets -- Fingerprinting-A Tool for Comparing Across Representations -- Definition of Fingerprints -- Example Fingerprint Computation -- Fingerprint Results -- The Uncontrolled Variable Discovery -- Experimental Design -- Experimental Results -- Conclusion -- Problems Beyond Representation -- Changing the Payoff Matrix Changes the Agents that Arise -- Design of Experiments -- Results and Discussion -- Conclusions -- Changing the Level of Resources -- Design of Experiments -- Results and Discussion -- Conclusions and Next Steps -- Algorithm Details Matter To Population Size, Elite Fraction, and Mutation Rate -- Algorithm Details Matter To Population Size, Elite Fraction, and Mutation Rate -- Design of Experiments -- Results and Discussion -- Conclusions and Next Steps -- Including Tags and Geography -- Grid-based nIPD and Agent Specifications -- Experimental Design -- Results -- Conclusions -- Does All This Happen Outside of Prisoner's Dilemma? -- Coordination Prisoner's Dilemma, Rock-Paper-Scissors, and Morphs -- Design of Experiments -- Results and Discussion -- Conclusions -- Divide-the-Dollar-A More Complex Game -- Generalize Divide-the-Dollar -- Design of Experiments -- Results and Discussion -- Conclusions and Next Steps -- The Snowdrift Game -- The Game Models -- Experimental Results -- Discussion -- Conclusions.
    Additional Edition: 9781681731629
    Additional Edition: Print version Kim, Eun-Youn On the Design of Game-Playing Agents San Rafael : Morgan & Claypool Publishers,c2017
    Language: English
    URL: Volltext  (lizenzpflichtig)
    Library Location Call Number Volume/Issue/Year Availability
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  • 10
    Online Resource
    Online Resource
    [San Rafael, California] : Morgan & Claypool
    UID:
    (DE-627)1655404296
    Format: 1 Online-Ressource (xiii, 172 pages)
    Series Statement: Synthesis lectures on games and computational intelligence
    Content: 1. Introduction -- 1.1 Prisoner's dilemma and other games -- 1.2 Digital evolution: evolving game players -- 1.2.1 Optimization vs. co-evolution -- 1.3 From checkers through the rise of the go machines -- 1.4 Why are the simple games so hard? --
    Content: 2. The first place where trouble arose -- 2.1 Representation: what is it? -- 2.1.1 Finite state machines, direct representation -- 2.1.2 Finite state machines with a cellular representation -- 2.1.3 Boolean formulas -- 2.1.4 Function stacks -- 2.1.5 ISAc lists -- 2.1.6 Markov chains and look-up tables -- 2.1.7 Artificial neural nets -- 2.2 Fingerprinting: a tool for comparing across representations -- 2.2.1 Definition of fingerprints -- 2.2.2 Example fingerprint computation -- 2.2.3 Fingerprint results -- 2.3 The uncontrolled variable discovery -- 2.3.1 Experimental design -- 2.3.2 Experimental results -- 2.3.3 Conclusion --
    Content: 3. Problems beyond representation -- 3.1 Changing the payoff matrix changes the agents that arise -- 3.1.1 Design of experiments -- 3.1.2 Results and discussion -- 3.1.3 Conclusions -- 3.2 Changing the level of resources -- 3.2.1 Design of experiments -- 3.2.2 Results and discussion -- 3.2.3 Conclusions and next steps -- 3.3 Algorithm details matter to population size, elite fraction, and mutation rate -- 3.3.1 Design of experiments -- 3.3.2 Results and discussion -- 3.3.3 Conclusions and next steps -- 3.4 Including tags and geography -- 3.4.1 Grid-based nIPD and agent specifications -- 3.4.2 Experimental design -- 3.4.3 Results -- 3.4.4 Conclusions --
    Content: 4. Does all this happen outside of prisoner's dilemma? -- 4.1 Coordination prisoner's dilemma, rock-paper-scissors, and morphs -- 4.1.1 Design of experiments -- 4.1.2 Results and discussion -- 4.1.3 Conclusions -- 4.2 Divide-the-dollar--a more complex game -- 4.2.1 Generalize divide-the-dollar -- 4.2.2 Design of experiments -- 4.2.3 Results and discussion -- 4.2.4 Conclusions and next steps -- 4.3 The snowdrift game -- 4.3.1 The game models -- 4.3.2 Experimental results -- 4.3.3 Discussion -- 4.3.4 Conclusions --
    Content: 5. Noise! -- 5.1 Noisy games are different -- 5.1.1 Experimental design -- 5.1.2 Analysis techniques -- 5.1.3 Results and discussion -- 5.1.4 Conclusions and next steps -- 5.2 Evolutionary velocity -- 5.2.1 Definition of evolutionary velocity -- 5.2.2 Analysis of evolutionary velocity --
    Content: 6. Describing and designing representations -- 6.1 Does the representation have internal states? -- 6.2 Does the representation use external state information? -- 6.3 Can the representation learn? -- 6.4 Does the representation use random numbers? -- 6.5 Complex representations --
    Content: Bibliography -- Authors' biographies
    Content: Evolving agents to play games is a promising technology. It can provide entertaining opponents for games like Chess or Checkers, matched to a human opponent as an alternative to the perfect and unbeatable opponents embodied by current artificial intelligences. Evolved agents also permit us to explore the strategy space of mathematical games like Prisoner's Dilemma and Rock-Paper-Scissors. This book summarizes, explores, and extends recent work showing that there are many unsuspected factors that must be controlled in order to create a plausible or useful set of agents for modeling cooperation and conflict, deal making, or other social behaviors. The book also provides a proposal for an agent training protocol that is intended as a step toward being able to train humaniform agents - in other words, agents that plausibly model human behavior
    Note: Includes bibliographical references (pages 161-170)
    Additional Edition: 9781681731629
    Additional Edition: 1681731630
    Additional Edition: 9781681731636
    Additional Edition: Erscheint auch als Druck-Ausgabe 9781681731629
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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