| 000 | 05945nam a2200673 i 4500 | ||
|---|---|---|---|
| 001 | 8011694 | ||
| 003 | IEEE | ||
| 005 | 20200413152925.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 170823s2017 caua foab 000 0 eng d | ||
| 020 |
_a9781681731636 _qebook |
||
| 020 |
_z9781681731629 _qprint |
||
| 024 | 7 |
_a10.2200/S00782ED1V01Y201706GCI002 _2doi |
|
| 035 | _a(CaBNVSL)swl00407749 | ||
| 035 | _a(OCoLC)1001572095 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aQA76.76.I58 _bK556 2017 |
|
| 082 | 0 | 4 |
_a006.3 _223 |
| 100 | 1 |
_aKim, Eun-Youn, _eauthor. |
|
| 245 | 1 | 0 |
_aOn the design of game-playing agents / _cEun-Youn Kim, Daniel Ashlock. |
| 264 | 1 |
_a[San Rafael, California] : _bMorgan & Claypool, _c2017. |
|
| 300 |
_a1 PDF (xiii, 172 pages) : _billustrations. |
||
| 336 |
_atext _2rdacontent |
||
| 337 |
_aelectronic _2isbdmedia |
||
| 338 |
_aonline resource _2rdacarrier |
||
| 490 | 1 |
_aSynthesis lectures on games and computational intelligence, _x2573-6493 ; _v# 2 |
|
| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 504 | _aIncludes bibliographical references (pages 161-170). | ||
| 505 | 0 | _a1. 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? -- | |
| 505 | 8 | _a2. 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 -- | |
| 505 | 8 | _a3. 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 -- | |
| 505 | 8 | _a4. 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 -- | |
| 505 | 8 | _a5. 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 -- | |
| 505 | 8 | _a6. 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 -- | |
| 505 | 8 | _aBibliography -- Authors' biographies. | |
| 506 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | ||
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 520 | 3 | _aEvolving 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. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF title page (viewed on August 23, 2017). | ||
| 650 | 0 | _aIntelligent agents (Computer software) | |
| 650 | 0 |
_aGames _xData processing. |
|
| 653 | _amathematical games | ||
| 653 | _agame-playing agents | ||
| 653 | _aautomatic training of agents | ||
| 653 | _arepresentation in evolutionary computation | ||
| 655 | 0 | _aElectronic books. | |
| 700 | 1 |
_aAshlock, Daniel, _eauthor. |
|
| 776 | 0 | 8 |
_iPrint version: _z9781681731629 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on games and computational intelligence ; _v# 2. _x2573-6493 |
|
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=8011694 |
| 999 |
_c562278 _d562278 |
||