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PART I 인공지능
CHAPTER 1 소개 3 1.1 인공지능이란 무엇인가? ·············································································· 4 1.2 인공지능의 기반 학문 ··············································································· 10 1.3 인공지능의 역사 ························································································ 25 1.4 인공지능의 현황 ························································································ 38 1.5 인공지능의 위험과 혜택 ············································································ 43 요약 ······································································································ 47 참고문헌 및 역사적 참고사항 ····························································· 49 CHAPTER 2 지능적 에이전트 51 2.1 에이전트와 환경 ························································································ 52 2.2 좋은 행동: 합리성 개념 ············································································ 55 2.3 환경의 본성 ······························································································· 59 2.4 에이전트의 구조 ························································································ 65 요약 ······································································································ 81 참고문헌 및 역사적 참고사항 ····························································· 82 PART II 문제 해결 CHAPTER 3 검색을 통한 문제 해결 87 3.1 문제 해결 에이전트 ··················································································· 88 3.2 문제의 예 ··································································································· 92 3.3 검색 알고리즘들 ························································································ 97 3.4 정보 없는 검색 전략 ··············································································· 104 3.5 정보 있는 검색(발견적 검색) 전략들 ····················································· 114 3.6 발견적 함수 ······························································································ 130 요약 ···································································································· 140 참고문헌 및 역사적 참고사항 ··························································· 141 CHAPTER 4 복잡한 환경의 검색 147 4.1 국소 검색과 최적화 문제 ········································································ 147 4.2 연속 공간의 국소 검색 ············································································ 157 4.3 비결정론적 동작들을 수반한 검색 ·························································· 161 4.4 부분 관측 가능 환경의 검색 ··································································· 167 4.5 온라인 검색 에이전트와 미지 환경 ························································ 177 요약 ···································································································· 185 참고문헌 및 역사적 참고사항 ··························································· 186 CHAPTER 5 대립 검색과 게임 191 5.1 게임 이론 ································································································· 191 5.2 게임의 최적 결정 ····················································································· 194 5.3 발견적 알파베타 트리 검색 ····································································· 203 5.4 몬테카를로 트리 검색 ·············································································· 210 5.5 확률적 게임 ······························································································ 214 5.6 부분 관측 가능 게임 ··············································································· 218 5.7 게임 검색 알고리즘들의 한계 ································································· 224 요약 ···································································································· 226 참고문헌 및 역사적 참고사항 ························································· 227 CHAPTER 6 제약 충족 문제 235 6.1 제약 충족 문제의 정의 ············································································ 236 6.2 제약 전파: CSP의 추론 ·········································································· 242 6.3 CSP를 위한 역추적 검색 ······································································· 250 6.4 CSP를 위한 국소 검색 ··········································································· 257 6.5 문제의 구조 ······························································································ 259 요약 ···································································································· 265 참고문헌 및 역사적 참고사항 ··························································· 266 PART III 지식, 추론, 계획 수립 CHAPTER 7 논리적 에이전트 273 7.1 지식 기반 에이전트 ················································································· 274 7.2 웜퍼스 세계 ······························································································ 276 7.3 논리 ·········································································································· 280 7.4 명제 논리: 아주 간단한 논리 ································································· 284 7.5 명제 정리 증명 ························································································ 291 7.6 효과적인 명제 모형 점검 ········································································ 304 7.7 명제 논리에 기초한 에이전트 ································································· 310 요약 ···································································································· 322 참고문헌 및 역사적 참고사항 ··························································· 323 CHAPTER 8 1차 논리 327 8.1 표현의 재고찰 ·························································································· 327 8.2 1차 논리의 구문과 의미론 ······································································· 333 8.3 1차 논리의 활용 ······················································································· 346 8.4 1차 논리의 지식 공학 ·············································································· 354 요약 ···································································································· 361 참고문헌 및 역사적 참고사항 ··························································· 362 CHAPTER 9 1차 논리의 추론 365 9.1 명제 추론 대 1차 추론 ············································································ 365 9.2 단일화와 1차 추론 ··················································································· 368 9.3 순방향 연쇄 ······························································································ 374 9.4 역방향 연쇄 ······························································································ 382 9.5 분해 ·········································································································· 389 요약 ···································································································· 403 참고문헌 및 역사적 참고사항 ··························································· 404 CHAPTER 10 지식 표현 409 10.1 온톨로지 공학 ························································································ 410 10.2 범주와 객체 ···························································································· 413 10.3 사건 ········································································································ 420 10.4 정신적 객체와 양상 논리 ······································································ 425 10.5 범주 추론 시스템 ··················································································· 429 10.6 기본 정보를 이용한 추론 ······································································ 434 요약 ·································································································· 440 참고문헌 및 역사적 참고사항 ························································· 441 CHAPTER 11 자동 계획 수립 449 11.1 고전적 계획 수립의 정의 ······································································ 450 11.2 고전적 계획 수립을 위한 알고리즘들 ··················································· 455 11.3 계획 수립을 위한 발견적 함수 ····························································· 460 11.4 위계적 계획 수립 ··················································································· 465 11.5 비결정론적 정의역에서의 계획 수립과 실행 ········································ 476 11.6 시간, 일정, 자원 ···················································································· 488 11.7 계획 수립 접근방식들의 분석 ······························································· 493 요약 ·································································································· 494 참고문헌 및 역사적 참고사항 ························································· 495 PART IV 불확실한 지식과 추론 CHAPTER 12 불확실성의 정량화 505 12.1 불확실성하에서의 행동 ·········································································· 505 12.2 기본적인 확률 표기법 ············································································ 510 12.3 완전 결합 분포를 이용한 추론 ····························································· 518 12.4 독립성 ····································································································· 522 12.5 베이즈 규칙과 그 용법 ·········································································· 523 12.6 단순 베이즈 모형 ··················································································· 528 12.7 웜퍼스 세계의 재고찰 ············································································ 530 요약 ·································································································· 534 참고문헌 및 역사적 참고사항 ························································· 535 CHAPTER 13 확률적 추론 539 13.1 불확실한 문제 영역의 지식 표현 ·························································· 539 13.2 베이즈망의 의미론 ················································································· 542 13.3 베이즈망의 정확 추론 ············································································ 558 13.4 베이즈망의 근사 추론 ············································································ 568 13.5 인과망 ····································································································· 585 요약 ·································································································· 591 참고문헌 및 역사적 참고사항 ························································· 591 CHAPTER 14 시간에 따른 확률적 추론 599 14.1 시간과 불확실성 ····················································································· 600 14.2 시간적 모형의 추론 ··············································································· 605 14.3 은닉 마르코프 모형 ··············································································· 615 14.4 칼만 필터 ······························································································· 622 14.5 동적 베이즈망 ························································································ 630 요약 ·································································································· 643 참고문헌 및 역사적 참고사항 ························································· 644 CHAPTER 15 확률적 프로그래밍 647 15.1 관계 확률 모형 ······················································································ 648 15.2 열린 모집단 확률 모형 ·········································································· 656 15.3 복잡한 세계의 추적 ··············································································· 665 15.4 확률 모형으로서의 프로그램 ································································· 670 요약 ·································································································· 676 참고문헌 및 역사적 참고사항 ························································· 676 CHAPTER 16 간단한 의사결정 683 16.1 불확실성하에서의 믿음과 욕구의 결합 ················································· 684 16.2 효용이론의 기초 ····················································································· 685 16.3 효용 함수 ······························································································· 689 16.4 다중 특성 효용 함수 ············································································· 699 16.5 의사결정망 ······························································································ 705 16.6 정보의 가치 ···························································································· 708 16.7 미지의 선호도 ························································································ 716 요약 ·································································································· 720 참고문헌 및 역사적 참고사항 ························································· 721 CHAPTER 17 복잡한 의사결정 727 17.1 순차적 의사결정 문제 ············································································ 727 17.2 MDP를 위한 알고리즘들 ······································································· 740 17.3 강도 문제 ······························································································· 750 17.4 부분 관측 가능 MDP ············································································ 759 17.5 POMDP를 푸는 알고리즘 ····································································· 762 요약 ·································································································· 768 참고문헌 및 역사적 참고사항 ························································· 769 CHAPTER 18 다중 에이전트 의사결정 775 18.1 다중 에이전트 환경의 특징 ··································································· 775 18.2 비협력 게임 이론 ··················································································· 783 18.3 협력 게임 이론 ······················································································ 809 18.4 집합적 의사결정 ····················································································· 818 요약 ·································································································· 835 참고문헌 및 역사적 참고사항 ························································· 836 찾아보기 ·················································· 843 |
저자 소개3
저스튜어트 러셀
관심작가 알림신청Stuart Russell
버클리에 있는 캘리포니아대학교 컴퓨터과학 교수이자 공학 부문 스미스자데이 석좌교수. 옥스퍼드대학교 웨덤 칼리지에서 물리학을 공부하고 스탠퍼드대학교에서 컴퓨터과학으로 박사학위를 받았다. 기계 학습, 확률론적 추론, 실시간 의사 결정, 계산 생리학 및 철학적 기초를 포함한 인공지능의 광범위한 주제를 놓고 연구했고, 지금은 자율무기의 위협, 인공지능의 장기적 미래 및 인류와의 관계 등에도 관심을 두고 있다. 미국 인공지능협회, 컴퓨터학회, 미국과학진흥협회 회원이며, 세계경제포럼의 AI와 로봇학 위원회 부의장, 유엔 군축 문제 고문도 맡고 있다. 2016 서울디지털포럼, 2020 서울포
버클리에 있는 캘리포니아대학교 컴퓨터과학 교수이자 공학 부문 스미스자데이 석좌교수. 옥스퍼드대학교 웨덤 칼리지에서 물리학을 공부하고 스탠퍼드대학교에서 컴퓨터과학으로 박사학위를 받았다. 기계 학습, 확률론적 추론, 실시간 의사 결정, 계산 생리학 및 철학적 기초를 포함한 인공지능의 광범위한 주제를 놓고 연구했고, 지금은 자율무기의 위협, 인공지능의 장기적 미래 및 인류와의 관계 등에도 관심을 두고 있다. 미국 인공지능협회, 컴퓨터학회, 미국과학진흥협회 회원이며, 세계경제포럼의 AI와 로봇학 위원회 부의장, 유엔 군축 문제 고문도 맡고 있다. 2016 서울디지털포럼, 2020 서울포럼 등에서 강연하기도 했다.
구글 리서치 디렉터 피터 노빅과 함께 『인공지능: 현대적 접근방식』(1995)을 썼다. AI 분야의 결정판 교과서로 널리 인정받고 있는 『인공지능』(현재 4판)은 13개 언어로 번역되어 118개국, 1,500여 대학에서 교재로 사용되고 있다. 2016년에는 UC 버클리를 중심으로 여러 대학과 기관이 협력하는 연구기관 ‘휴먼컴패터블 AI센터’를 설립하여 AI 연구의 일반적인 추진 방향을 증명 가능하게 유익한 AI 시스템 쪽으로 재설정하는 데 필요한 개념적·기술적 도구를 개발해왔고, 그 결과물을 『어떻게 인간과 공존하는 인공지능을 만들 것인가: AI와 통제 문제』에 담았다.
구글 리서치 디렉터 피터 노빅과 함께 『인공지능: 현대적 접근방식』(1995)을 썼다. AI 분야의 결정판 교과서로 널리 인정받고 있는 『인공지능』(현재 4판)은 13개 언어로 번역되어 118개국, 1,500여 대학에서 교재로 사용되고 있다. 2016년에는 UC 버클리를 중심으로 여러 대학과 기관이 협력하는 연구기관 ‘휴먼컴패터블 AI센터’를 설립하여 AI 연구의 일반적인 추진 방향을 증명 가능하게 유익한 AI 시스템 쪽으로 재설정하는 데 필요한 개념적·기술적 도구를 개발해왔고, 그 결과물을 『어떻게 인간과 공존하는 인공지능을 만들 것인가: AI와 통제 문제』에 담았다.
스튜어트 러셀의 다른 상품
저피터 노빅
관심작가 알림신청Peter Norvig
구글의 연구실장이며, 2002년에서 2005년까지 핵심 웹 검색 엔진 개발을 이끌었다. 전에는 NASA Ames Research Center의 계산 과학 분과장으로서 NASA의 인공지능 및 로봇공학 연구와 개발을 감독했다. 서던 캘리포니아 대학교의 교수였으며, 버클리 대학교와 스탠퍼드 대학교의 연구교수단 일원이었다. 그의 다른 책으로는 《Paradigms of AI Programming: Case Studies in Common Lisp》와 《Verbmobil: A Translation System for Face?to?Face Dialog》, 그리고 《Intelligent He
구글의 연구실장이며, 2002년에서 2005년까지 핵심 웹 검색 엔진 개발을 이끌었다. 전에는 NASA Ames Research Center의 계산 과학 분과장으로서 NASA의 인공지능 및 로봇공학 연구와 개발을 감독했다. 서던 캘리포니아 대학교의 교수였으며, 버클리 대학교와 스탠퍼드 대학교의 연구교수단 일원이었다. 그의 다른 책으로는 《Paradigms of AI Programming: Case Studies in Common Lisp》와 《Verbmobil: A Translation System for Face?to?Face Dialog》, 그리고 《Intelligent Help Systems for UNIX》가 있다.
피터 노빅 의 다른 상품
역류광
관심작가 알림신청
커누스 교수의 『컴퓨터 프로그래밍의 예술』 시리즈를 비롯해 90여 권의 다양한 IT 전문서를 번역한 전문 번역가이다. 이 책과 연관된 번역서로는 『딥러닝을 위한 수학』 『파이썬으로 배우는 자연어 처리 인 액션』 (이안 굿펠로의) 『심층 학습』 등이 있다.
류광의 다른 상품
품목정보
- 발행일
- 2021년 08월 25일
- 쪽수, 무게, 크기
- 932쪽 | 188*245*39mm
- ISBN13
- 9791191600315
책 속으로
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앨런 튜링이 제안한(Turing, 1950) 튜링 검사(Turing test)는 “기계가 생각할 수 있는가?”라는 질문의 철학적 모호함을 피하는 하나의 사고 실험으로 고안되었다. 인간 조사자가 글로 쓴 질문에 대해 컴퓨터가 글로 답을 했을 때, 만일 그 답이 컴퓨터가 제출한 것인지 아니면 인간이 제출한 것인지 인간 조사자가 구분하지 못한다면 그 컴퓨터는 튜링 검사를 통과한 것이다.
--- p.4 필자는 어느 날 샹젤리제 거리를 걷다가 도로 건너편에서 옛 친구를 만난다. 오가는 차가 없고 특별히 할 일도 없어서, 합리적인 판단하에 도로를 건너기 시작한다. 그런데 3만 3천 피트 상공에서 화물 항공기의 화물실 문이 떨어져서, 필자는 결국 도로를 다 건너지 못하고 납작해진다... 도로를 건너기로 한 것이 비합리적이었을까? 필자의 부고에 “멍청하게도 도로를 건너려 했다.”라는 문구가 포함될 가능성은 작다. 이 예는 합리성이 완벽함과 같은 것이 아님을 보여 준다. --- p.57 물론 그런 에이전트는 취약하다. 비천한 쇠똥구리를 생각해 보자. 땅에 구멍을 파서 알을 낳은 후 쇠똥구리는 근처의 배설물을 공 모양으로 만들어서 둥지의 구멍을 막는다. 그런데 둥지로 공을 굴리고 가는 도중에 공을 빼내면 쇠똥구리는 그 사실을 인식하지 못하고, 존재하지 않는 공을 굴려서 구멍을 막는 ‘팬터마임’을 한다. 진화에 의해 쇠똥구리의 행동에 하나의 가정이 삽입되었고, 그 가정이 위반되면 결과적으로 성공적이지 못한 행동이 빚어지는 것이다. --- p.58 계획 감시 덕분에, 무려 480여 쪽 분량의 논의 후에 드디어 쇠똥구리(p.58)보다 더 똑똑한 에이전트가 등장했다! 계획 감시 에이전트는 자신이 쇠똥 공을 잡고 있지 않음을 감지하고, 다른 공을 구해서 구멍에 끼워 넣을 계획을 다시 수립할 것이다. --- p.487 게임 이론은 게임 초기에는 다른 플레이어의 전략을 아직 알 수 없다는 개념을 나타내는 데 아주 적합하다. 그러나, 다른 플레이어들이 완전히 합리적이지는 않은 경우, 게임 이론은 다음에 할 일을 알려 주지 않는다. 베이즈-내시 균형(Bayes-Nash equilibrium)이라는 개념은 이 문제를 부분적으로 처리한다. 베이즈 내시 균형은 다른 플레이어들의 전략들에 대한 플레이어의 사전 확률분포를 기준으로 한 균형이다. 다른 말로 하면, 이 균형은 다른 플레이어들이 채용할 가능성이 있는 전략들에 대한 플레이어의 믿음들을 대표한다. --- p.867 |
출판사 리뷰
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2010년대 인공신경망의 부활과 심층학습의 눈부신 성과를 반영한 인공지능 연구의 결정판!
2016년에 나온 제3판 번역서(2009년에 출간된 원서 3판을 번역)는 최신 연구 반영에 한계가 있었으나, 이번 제4판 번역서는 최근 성과(자연어 이해, 로봇공학, 컴퓨터 시각에 심층학습이 끼친 영향, 강화학습을 로봇공학에 적용하는 방법, 기계학습, 인공지능 윤리 등)를 충실하게 반영한, 2020년에 출간된 원서를 옮긴 것이라 이 분야의 ‘좀 더 통합된 상’을 원하는 여러 독자의 갈증을 해소하는 데 큰 도움이 될 것입니다. 전체적으로, 책의 약 25%가 완전히 새로운 내용이고 나머지 75%도 이 분야의 좀 더 통합된 상을 제시하기 위해 크게 변경되었으며, 이번 판에서 인용한 문헌의 22%는 2010년 이후에 출판된 것들입니다. 제4판에서 새로운 점들 □ 사람이 손으로 짜는 지식 공학보다는 기계학습에 좀 더 무게를 실었다. 기계학습은 가용 데이터와 컴퓨팅 자원이 증가하고 새로운 알고리즘들이 등장한 덕분에 큰 성공을 거두고 있다. □ 심층학습, 확률적 프로그래밍, 다중 에이전트 시스템을 각각 개별적인 장(챕터)으로 두어서 좀 더 자세히 다룬다. □ 자연어 이해, 로봇공학, 컴퓨터 시각에 관한 장들을 심층학습이 끼친 영향을 반영해서 수정했다. □ 로봇공학 장에 사람과 상호작용하는 로봇에 관한 내용과 강화학습을 로봇공학에 적용하는 방법에 관한 내용이 추가되었다. □ 이전에는 인공지능의 목표를 사람이 구체적인 효용 정보(목적함수)를 제공한다는 가정하에서 기대 효용을 최대화하려는 시스템을 만드는 것이라고 정의했다. 그러나 이번 판에서는 목적함수가 고정되어 있으며 인공지능 시스템이 목적함수를 알고 있다고 가정하지 않는다. 대신, 시스템은 자신이 봉사하는 인간의 진짜 목적이 무엇인지 확실하게 알지 못할 수 있다고 가정한다. 시스템은 반드시 자신이 무엇을 최대화할 것인지를 배워야 하며, 목적에 관해 불확실성이 존재하더라도 적절히 작동해야 한다. □ 인공지능이 사회에 미치는 영향을 좀 더 자세하게 다루었다. 여기에는 윤리, 공정성, 신뢰, 안정성에 관한 핵심적인 문제들을 고찰한다. □ 각 장 끝의 연습문제들을 온라인 사이트로 옮겼다. 덕분에 강사들의 요구와 이 분야 및 인공지능 관련 소프트웨어 도구의 발전에 맞게 연습문제들을 계속 추가, 갱신, 개선할 수 있게 되었다. |
