000			04040nam a2200493 a 4500
003			OSt
005			20230630165324.0
008			230630s2020 us d|||| |||| 00| 0 eng d
020			_a9781683925163
040			_aCo-BrCUA _bspa _eRCAA2 _cCo-BrCUA
082	0	4	_a006.31 _bG977 2020 _223
100			_aGupta, Neeru _934723
245			_aArtificial intelligence basics / _cNeeru Gupta, Ramita Mangla
260			_aEstados unidos : _bMercury learning, _c2020
300			_a203 páginas, _bIlustraciones; _c23 cm
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aSin mediación _bn
338			_2rdacarrier _aVolumen _bnc
505			_a1. ARTIFICIAL INTELLIGENCE.
505			_a1.1 Computerized reasoning. 1.2 Turing test. 1.3 What is intelligence?. 1.4 Artificial intelligence. 1.5 Goals of artificial intelligence. 1.6 History of artificial intelligence. 1.6 History of artificial intelligence. 1.7 Advantages of artificial intelligence. 1.8 Application areas of artificial intelligence.
505			_a2. PROBLEM REPRESENTATION.
505			_a2.1 Introduction. 2.2 Problem characteristics. 2.3 Problem representation in AI. 2.4 Production system. 2.5 conflict resolution.
505			_a3. THE SEARCH PROCESS.
505			_a3.1 Search process. 3.2 Strategies for search. 3.3 Search techniques.
505			_a4.GAME PLAYING.
505			_a4.1 Game playing. 4.2 Game tree. 4.3 Components of game playing program. 4.4 Game playing strategies. 4.5 Problems in computer game playing programs.
505			_a5.KNOWLEDGE REPRESENTATION.
505			_a5.1Introduction. 5.2 Definition of knowledge. 5.3 Importance of knowledge. 5.4 Knowledge-based systems. 5.5 Differences between knowledge-based system and database systems. 5.6 Knowledge representation scheme.
505			_a6. EXPERT SYSTEMS.
505			_a6.1 Introduction. 6.2 Definition of an expert system. 6.3 Characteristics of an expert system. 6.4 Architecture of expert systems. 6.5 Expert system life cycle. 6.6 Knowledge engineering process. 6.7 knowledge acquisition. 6.8 Difficulties in knowledge acquisition. 6.9 Knowledge acquisition strategies. 6.10 Advantages of expert systems. 6.11 Limitations of expert systems. 6.12 Examples of expert systems.
505			_a7. LEARNING.
505			_a7.1 Learning. 7.2 general model for machine learning systems. 7.3 Characteristics of machine learning. 7.4 Types of learning. 7.5 Advantages of machine learning. 7.6 Disadvantages of machine learning.
505			_a8. PROLOG.
505			_a8.1 Preliminaries of prolog. 8.2 Milestones in prolog language development. 8.3 What is a horn clause?. 8.4 Robinson´s resolution rule. 8.5 Parts of a prolog program. 8.6 Queries to a database. 8.7 How does prolog solve a query?. 8.8 Compound queries. 8.9 The_variable. 8.10 Recursion in prolog. 8.12 Head and tail of a list. 8.13 Print all the members of the list. 8.14 Print the list in reverse order. 8.15 Appending a list. 8.16 Find whether the given item is a member of the list. 8.17 Finding the length of the list. 8.18 Controlling execution in prolog. 8.19 About turbo prolog.
505			_a9. PYTHON.
505			_a9.1 Languages used for building AI. 9.2 Why do people choose python?. 9.3 Build AI using python. 9.4 Running python. 9.5 Pitfalls. 9.6 Features of python. 9.7 Useful libraries. 9.8 Utilities. 9.9 Testing code.
505			_a10. ARTIFICIAL INTELLIGENCE MACHINES AND ROBOTICS.
505			_a10.0 Introduction. 10.1 History serving, emulating, enhancing, and replacing man. 10.2 Technical issues. 10.3 Applications: robotics in the twenty-first century. 10.4 Summary.
650	1	0	_aArtificial intelligence - _xBasics _934724
650	1	0	_aArtificial intelligence - _xLenguaje de programación _934725
650	1	0	_aArtificial intelligence - _xAutoaprendizaje _934726
700			_aMangla, Ramita _934727
942			_2ddc _cRS _i006.31 G977 2020
999			_c67668 _d67668