1 Foundations of Artificial Intelligence (Overview) artificial intelligence; AI Logic Theorist 1956 Dartmouth Conference Turing test image recognition
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2 1 Foundations of Artificial Intelligence (Overview) artificial intelligence; AI Logic Theorist 1956 Dartmouth Conference Turing test image recognition speech recognitionnaturallanguage understanding ; ontology deep learning machine learning
3 AI AI AI 1956 [1-b] 29 J. McCarthy AI M. Minsky H. SimonA. Newell C. Shannon AI Logic Theorist J. C. Shaw means-ends analysis general problem solver; GPS 1957 AI machine translation N. Chomsky generative grammar J. von Neumann 2 [1] A. Turing1950 [2] W. McCulloch W. Pitts neural network; NN 1950
4 AI combinatorial explosion heuristics [1-c] A* A* algorithm [1-05] planning STRIPS [1-14] AI J. Weizenbaum ELIZA 1966 National Research Council ALPAC ALPAC report [8-a] 1968 semantic network 1960 Lisp list processor AI A. Samuel H. Gelerntertheorem proving in geometry J. Slagle symbolic integration 1961 [3] AI 1962 F. Rosenblatt perceptron [4] 1963 MIT MAC Multiple Access Computing Machine Aided CognitionAI SRI Stanford Research Institute AI computer vision 1965 MIT L. Roberts IJCAI AI DEN- DRAL E. A. Feigenbaum B. Buchanan MIT MACSYMA J. Moses 10 expert system; ES AI frame blackboard model AI 1970
5 AI 1970 AI 1972 T. Winograd SHRDLU [5]R. Schank1969 concept dependency script ARPA R. Reddy HEARSAY-II HARPY HEARSAY-II 1970 MYCIN E. H. Shortliffe AI 1977 knowledge engineering AI J. P. McDermott DEC R1 XCON 1980 AI 1979 American Association for Artificial Intelligence; AAAI 1986 Japanese Society for Artificial Intelligence 5 fifth generation computer Prolog Prolog J. A. Robinsonresolution principle Prolog A. Colmerauer R. Kowalski Prolog
6 6 1 / non-monotonic reasoning common sense D. Lenat CYC CYC project CYC encyclopedia 1984 EDR CYC constraint satisfaction problem; CSP constraint CSP analogy 1983 D. Gentner 1980 case-based reasoning; CBR memory-based reasoning; MBR [6] 1980 [7] AI connectionist MYCIN certainty factor 1988 J. Pearl Bayesian network; BN belief network causal network L. Zadeh 1965 fuzzy set AI 1960 AI 1980 AI 1986 Society of Mind [8] 1980 AI
7 7 emergence artificial life; Alife genetic algorithm; GA genetic programming; GP deduction induction inductive learning knowledge based system knowledge acquisition bottleneck 1960 AI 1960 checkers program rote learning R. Quinlan ID3 iterative dichotomiser 3 C4.5 decision tree 1970 P. H. Winston 1970 R. S. Michalski 1978 T. Michell version space Prolog 1981 E. Y. Shapiro model inference system 1990 S. Muggleton inductive logic programming; ILP 1990 data mining 1980 deductive learning ; explanation-based learning; EBL 1980 computational learning theory 1970 P. Langley BACON 1976 AM EURISKO 1990 AI WWW 20 AI
8 8 1 knowledge discovery 1990 agent1990 autonomous robot1990 embodied intelligence 1997 RoboCup R. A. Brooks subsumption architecture intelligence without representation 1997 IBM Deep Blue G. KasprovAI 40 AI 21 AI 2006 IBM Watson 2011! Jeopardy! Watson syntactic analysis Wikipedia Watson 2012 Geoffrey Hinton deep learning AlphaGo Ray Kurzweil [9] singularity zero-sum perfect-information game
9 9 perfect-information game autonomous driving zerosum game non-zero-sum game AI 21 AI AI AI 9 AI AI AI AI ontology understanding AI AI BPR business process reengineering; AI AI H. Dreyfus AI AI [10] J. R. SearleChinese room [11] AI weak AI AI strong AI R. Penrose [12] 1930 K. Gödel incompleteness theoremai Artificial Intelligence: A Modern Approach [13]
10 10 1 [14] The Handbook of Artificial Intelligence [15] I III 1970 Encyclopedia of Artificial Intelligence [16] 1990 [1] von Neumann, J. and Morgenstern, O. Theory of Games and Economic Behavior. Princeton University Press, 1944.,,,.., [2] Turing, A. M. Computer Machinery and Intelligence. Mind, Vol. 59, pp , [3] Minsky, M. Steps Toward Artificial Intelligence. In Proc. IRE, pp. 8 30, [4] Rosenblatt, F. Principles of neurodynamics; perceptrons and the theory of brain mechanisms. Spartan Books, Washington DC, [5] Winograd, T. Understanding Natural Language. Academic Press, 1972.,,.. 6,, [6] Minsky, M. and Papert, S. Perceptrons: An Introduction to Computational Geometry. The MIT Press, [7] Rumelhart, D. E., McClelland, J. L., and PDP Research Group. Parallel Distributed Processing Explorations in the Microstructure of Cognition: Foundations. The MIT Press, PDP., [8] Minsky, M. The Society of Mind. Simon and Schuster, New York, , [9] Kurzweil, R. The Singularity Is Near: When Humans Transcend Biology. Viking Books, New York, 2005.,,,.. NHK, [10] Dreyfus, H. What Computers Can t Do: The Limits of Artificial Intelligence. The MIT Press, 1972.,.., [11] Searle, J. Minds, Brains, and Programs. Behavioral and Brain Sciences, Vol. 3, No. 3, pp , 1980.,..., [12] Penrose, R. The Emperor s New Mind: Concerning Computers, Minds and The Laws of Physics. Oxford University Press, , [13] Russell, S. and Norvig, P. Artificial Intelligence: A Modern Approach. 3rd Edition. Pearson, Cambridge, UK, , [14].., [15] Barr, A. and Feigenbaum, E. A., editors. The Handbook of Artificial Intelligence. Vol. I, II, III. William Kaufmann, 1981.,. I III., [16] Shapiro, S. C., editor. Encyclopedia of Artificial Intelligence. 2nd Edition. John Wiley & Sons, , 1991.
11 A A* A* algorithm 4 ALPAC ALPAC report 4 AM AM 7 B BACON 7 BPR (business process reengineering) 9 C CYC CYC project 6 E EURISKO EURISKO 7 I IBM Watson IBM Watson 8 ID3 (iterative dichotomiser 3) 7 L Lisp (list processor) 4 Logic Theorist Logic Theorist 2 W Wikipedia Wikipedia 8 AlphaGo 8 rote learning 7 go 8 general problem solver; GPS 3 genetic algorithm; GA 7 genetic programming; GP 7 semantic network 4 understanding 9 causal network 6 agent 8 expert system; ES 4 deduction 7 deductive learning 7 speech recognition 2 ontology 2, 9 concept dependency 5 certainty factor 6 image recognition 2 perfect-information game 9 machine learning 2 machine translation 3 theorem proving in geometry 4 symbolic integration 4 induction 7 inductive learning 7 inductive logic programming; ILP 7 combinatorial explosion 4 computational learning theory 7 decision tree 7
12 12 blackboard model 4 (Society of Mind) 6 connectionist 6 computer vision 4 natural-language understanding 2 autonomous driving 9 means-ends analysis 3 common sense 6 autonomous robot 8 case-based reasoning; CBR 6 singularity 8 artificial life; Alife 7 artificial intelligence; AI 2 Japanese Society for Artificial Intelligence 5 deep learning 2 embodied intelligence 8 belief network 6 script 5 generative grammar 3 constraint 6 constraint satisfaction problem; CSP 6 explanation-based learning; EBL 7 zero-sum perfectinformation game 8 zero-sum game 9 emergence 6 Dartmouth Conference 2 5 fifth generation computer 5 checkers program 7 knowledge acquisition bottleneck 7 knowledge engineering 5 knowledge discovery 8 knowledge based system 7 Chinese room 9 Turing test 2 AI strong AI 9 Deep Blue 8 deep learning 8 data mining 7 syntactic analysis 8 resolution principle 5 neural network; NN 3 version space 7 perceptron 4 non-zero-sum game 9 non-monotonic reasoning 6 heuristics 4 intelligence without representation 8 fuzzy set 6 incompleteness theorem 9 planning 4 frame 4 Bayesian network; BN 6
13 13 subsumption architecture 8 memory-based MBR 6 reasoning; model inference system 7 resolution principle 5 AI weak AI 9 analogy 6 RoboCup 8
14 A A* algorithm A* 4 agent 8 AI (artificial intelligence) 2 Alife (artificial life) 7 ALPAC report ALPAC 4 AlphaGo 8 AM AM 7 analogy 6 artificial intelligence (AI) 2 artificial life (Alife) 7 autonomous driving 9 autonomous robot 8 B BACON 7 Bayesian network (BN) 6 belief network 6 blackboard model 4 BN (Bayesian network) 6 BPR (business process reengineering) 9 business process reengineering (BPR) 9 C case-based reasoning (CBR) 6 causal network 6 CBR (case-based reasoning) 6 certainty factor 6 checkers program 7 Chinese room 9 combinatorial explosion 4 common sense 6 computational learning theory 7 computer vision 4 concept dependency 5 connectionist 6 constraint 6 constraint satisfaction problem (CSP) 6 CSP (constraint satisfaction problem) 6 CYC project CYC 6 D Dartmouth Conference 2 data mining 7 decision tree 7 deduction 7 deductive learning 7 Deep Blue 8 deep learning 8 deep learning 2 E EBL (explanation-based learning) 7 embodied intelligence 8 emergence 6 ES (expert system) 4 EURISKO EURISKO 7 expert system (ES) 4 explanation-based learning (EBL) 7 F fifth generation computer 5 5
15 15 frame 4 fuzzy set 6 G GA (genetic algorithm) 7 general problem solver (GPS) 3 generative grammar 3 genetic algorithm (GA) 7 genetic programming (GP) 7 go 8 GP (genetic programming) 7 GPS (general problem solver) 3 H heuristics 4 I IBM Watson IBM Watson 8 ID3 (iterative dichotomiser 3) 7 ILP (inductive logic programming) 7 image recognition 2 incompleteness theorem 9 induction 7 inductive learning 7 inductive logic programming (ILP) 7 intelligence without representation 8 iterative dichotomiser 3 (ID3) 7 J Japanese Society for Artificial Intelligence 5 K knowledge acquisition bottleneck 7 knowledge based system 7 knowledge discovery 8 knowledge engineering 5 L Lisp (list processor) 4 list processor (Lisp) 4 Logic Theorist Logic Theorist 2 M machine learning 2 machine translation 3 MBR (memory-based reasoning) 6 means-ends analysis 3 memory-based reasoning (MBR) 6 model inference system 7 N natural-language understanding 2 neural network (NN) 3 NN (neural network) 3 non-monotonic reasoning 6 non-zero-sum game 9 O ontology 2, 9 P perceptron 4 perfect-information game 9 planning 4 R resolution principle 5 resolution principle 5 RoboCup 8 rote learning 7 S script 5
16 16 semantic network 4 singularity 8 Society of Mind 6 speech recognition 2 strong AI AI 9 subsumption architecture 8 symbolic integration 4 syntactic analysis 8 T theorem proving in geometry 4 Turing test 2 U understanding 9 V version space 7 W weak AI AI 9 Wikipedia Wikipedia 8 Z zero-sum game 9 zero-sum perfect-information game 8
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