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There are books which are the standard introduction into artificial intelligence. however, since those books have many pages, and since it is too extensive and costly for most students, the requirements for writing this book were clear: it should be an accessible introduction to modern artificial intelligence for self-study, with at most 250 pages. the course of artificial intelligence is taken by all engineering undergraduate and postgraduate students pursuing computer science. apart from this, it is a popular elective in almost all other branches of engineering. it is also a field chosen for research by many doctoral students. during the course of teaching artificial intelligence, the author had found that no textbook covers both artificial intelligence (ai) with intelligent systems (is) and soft computing in a comprehensive manner for beginner. this book provides a comprehensive coverage of the fundamental concepts and techniques in artificial intelligence. the main emphasis is on the solution of real world problems using the latest ai techniques. about the author after b-tech, anindita das bhattacharjee started her career in industry as a trainee software developer for a year. she had to quit industry career as she wanted to pursue higher studies. she has done m-tech in computer science from national institute of technology (nit), durgapur. she secured a position of first class second in m-tech. she has done many projects on data clustering, fuzzy logic, multi-objective genetic algorithm on time table problem, which are basically a part of artificial intelligence & soft computing. she has been teaching for the last 5 years in computer science as an asst. professor. her area of interest includes genetic algorithm, data clustering, computer graphics design, design and analysis of algorithm and distributed operating system. she started her teaching career in bengal college of engineering & technology (bcet) durgapur, as an asst. professor. currently she is working in swami vivekananda institute of science & technology (svist) kolkata, as an asst. professor. in computer science department. table of contents: 1. why to study artificial intelligence? 1.1 role of ai in engineering 1.2 ai in daily life 1.3 intelligence and artificial intelligence 1.3.1 components of artificial intelligence 1.3.2 different categories of ai 1.3.3 approaches to ai 1.4 different task domains of ai 1.5 history and early works of ai 1.5.1 new born ai 1.5.2 era of logic in ai 1.5.3 ai with modern aspects 1.6 history of ai in nutshell 1.7 programming methods 1.8 limitations of ai 2. why to study agents? 2.1 agent 2.1.1 desirable properties of an agent 2.1.2 example of agents 2.1.3 mathematical representation of agent 2.2 performance evaluation 2.3 task environment of an agent 2.3.1 task environment properties 2.4 agent’s classification 2.4.1 autonomous agents classification of autonomous agents logical agents and knowledge based agents 5 agent architecture 2.5.1 table based architecture 2.5.2 logic based architecture 2.5.3 knowledge-level architecture 2.5.4 layered architecture 3. logic 3.1 logic programming 3.2 logic representation 3.3 propositional logic 3.3.1 connective or operator 3.3.2 truth value 3.3.3 tautologies 3.3.4 contradictions 3.3.5 contingencies 3.3.6 antecedent and consequent 3.3.7 argument 3.3.8 resolution 3.3.9 horn clauses 3.3.10 applications of propositional logic 3.4 predicate logic and predicate calculus 3.4.1 syntax and symbols constant symbols function symbols semantic terms well formed formula (wff) quantifiers universal quantifier existential quantifier uniqueness quantifier 3.4.2 universe of discourse 3.4.3 applications 3.4.4 predicate calculus using inference 3.5 forward and backward chaining 3.5.1 basic backward chaining procedure 3.6 unification 3.6.1 the unification algorithm 3.7 resolution 3.7.1 resolution strategy conversion to normal form or clausal form conversion to clausal form 4. fundamental problem of logic 4.1 monotonicity with “flying-penguin” example 4.2 general disadvantage of monotonicity property in logic 4.2.1 solution 4.2 logic in the search space problem 4.3 logic in the decidability and incompleteness 4.4 logic in uncertainty modeling 5. search techniques 5.1 introduction to search 5.2 what is search? 5.3 representation techniques of search (graph and tree) or structure of state space search 5.4 categories of search 5.4.1 state space search strategies for exploration of problem space data –driven search strategy goal-driven search strategy disadvantage of state space search 5.5 issues in the design of search programs 5.5.1 forward and backward reasoning 5.5.2 matching 5.5.3 node representation 5.6 general search examples 5.6.1 example: tic-tac-toe problem 5.6.2 example: water-jugs-problem state representation and initial state operators solution example: state-space graph for water-jugs-problem 5.6.3 example: 8-puzzle problem 5.6.4 classification of search diagram representation 5.7 uninformed search or blind search 5.7.1 uniform-cost search 5.7.2 bidirectional search 5.8 informed search 5.8.1 heuristic method and heuristic search admissibility monotonicity or consistency informedness completeness dominance optimality heuristic functions hill climbing method and hill climbing search disadvantage of hill climbing method simulated annealing best-first search disadvantage branch-and-bound search a* search properties of a* search 6. game tress or game playing 6.1 introduction to two player games 6.1.1 two player game minimax search 6.1.2 what is nim? computing successor for nim 6.1.2 alpha-beta pruning rules for alpha-beta pruning alpha-beta concept search graph of alpha-beta concept missionaries and cannibals problem the problem solution 7. uncertainty in artificial intelligence 7.1 origin of uncertainty in artificial intelligence 7.2 probabilistic reasoning 7.2.1 application of probabilistic reasoning 7.2.2. reasoning under uncertainty probability and probability calculus bayes’ theorem and bayesian probabilistic inference bayes’ rule and application of bayes’ rule application of bayes’ rule disadvantage of bayesian probabilistic inference 7.3 dempster-shafer theory 7.3.1 advantage of dempster-shafer theory 7.3.2 disadvantage of dempster-shafer theory 7.4 bayesian networks (bns) 7.4.1 structure of bayesian networks 7.4.2 reasoning with bayesian networks 7.4.3 application of bayesian networks 7.4.4 disadvantages of bayesian networks 8. fuzzy set and fuzzy logic 8.1 different fuzzy operations 8.2 why we need fuzzy logic? 8.3 fuzzy system and design 8.4 fuzzy inference 8.5 rules of inference 8.6 fuzzification 8.6.1 how to use the concept of fuzzification? 8.7 fuzzy rules of inference or compositional rules of inference 8.8 defuzzification techniques 8.8.1 center of gravity ( cog) defuzzification 8.8.2 mean of maximum (mom) defuzzification 8.9 fuzzy logic, uncertainty and probability 8.10 advantages of fuzzy logic 8.11 limitations of fuzzy logic 8.12 application of fuzzy logic 9. knowledge representation 9.1 why we need knowledge? 9.2 need to represent knowledge 9.3 knowledge representation with mapping scheme 9.4 properties of a good knowledge base system 9.4.1 representational adequacy 9.4.2 inferential adequacy 9.4.3 inferential efficiency 9.4.4 acquisitional efficiency 9.5 types of knowledge 9.5.1 relational knowledge 9.5.2 inheritable knowledge 9.5.3 inferential knowledge 9.5.4 declarative knowledge 9.5.5 procedural knowledge 9.6 knowledge representation schemes 9.7 semantic net or associative nets 9.7.1 structure of semantic net 9.7.2 advantage of semantic net 9.7.3 disadvantages of semantic net 9.8 frames 9.8.1 structure of frames 9.8.2 advantages of using frame structure 9.9 conceptual graphs 10. advancement of artificial intelligence 10.1 expert system 10.2 expert system structure 10.3 knowledge acquisition 10.4 knowledge representation 10.5 inference control mechanism 10.6 user interface 10.7 expert system shell 10.8 knowledge representation 10.9 inference mechanism 10.10 developer interface and user interface 10.11 characteristics of expert system 10.11.1 expertise 10.11.2 understandable 10.11.3 high performance 10.11.4 flexibility 10.12 advantages of an expert system 10.12.1 permanence 10.12.2 reliability 10.12.3 availability 10.13 production system 10.13.1 components of production system 10.13.2 how production system works 10.13.3 advantage of using rule-based production system 10.13.4 disadvantage of using rule-based production system 10.14 artificial neural networks (ann) 10.14.1 characteristics of neural networks 10.14.2 architecture of neural networks feed forward or back propagation model recurrent network 10.14.3 learning or training methods and ann supervised learning unsupervised learning reinforced learning 10.14.4 types of neural networks multilayer perception (mlp) radial basis function networks (rbf) kohonen self organizing feature maps (sofm) 10.14.5 application of neural networks 11. planning 11.1 necessity of planning 11.2 planning agents 11.3 planning –generating schemes 11.3.1 non-hierarchical planning 11.3.2 hierarchical planning 11.3.3 script-based planning 11.3.4 opportunistic planning 11.4 algorithm for planning 11.5 planning representation with strips an example 11.6 difficulties with planning 12. constraint satisfaction problem (csp) 12.1 constraint satisfaction problem 12.2 constraints and satisfiabillity 12.3 basic search strategies for solving csp 12.3.1 generate and test 12.3.2 backtracking limitation of backtracking method 12.3.3 consistence driven techniques 12.3.4 forward checking 12.4 representation of csp problem 12.5 examples of constraint satisfaction problem 12.5.1 scheduling problem 12.5.2 n-queens problem 13. natural language processing 13.1 need for natural language processing 13.1.1 natural language understanding syntax semantics pragmatics 13.1.2 parsing context free grammar types of parsing 13.2 natural language generation 13.2.1 utterance planning 13.2.2 sentence planning 13.2.3 sentence generation 13.2.4 morphology 13.3 applications of natural language processing 14. genetic algorithms 14.1 single-objective genetic algorithm (sga) 14.2 multi-objective genetic algorithms 14.2.1 definitions to understand multi-objective genetic algorithm 14.2.2 design issues and components of multi objective genetic algorithms fitness function diversity elitism 14.3 nsga-ii 14.3.1 population initialization 14.3.2 non-dominated sort 14.3.3 density estimation 14.4 nsga-ii algorithm 15. prolog 15.1 prolog programming features 15.1.1 facts syntax of fact 15.1.2 rules syntax of rule 15.2 list 15.2.1 operations on list 15.3 structure 15.4 some solutions using turbo prolog 15.4.1 sample program hints write a prolog program to compute fibonacci term using recursion write a prolog program to compute greatest common divisor of two numbers write a prolog program to compute factorial of a positive number using recursion write a prolog program to concatenate two given list write a prolog program to find out the reverse of a given list

Author : Anindita das bhattacharjee
Publication : Spd
Isbn : 9789351100898
Store book number : 105
NRS 560.00
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