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| ARTIFICIAL INTELLIGENCE & SOFT COMPUTING FOR BEGINNERS |
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
2.4.1.1 classification of autonomous agents
2.4.1.2 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
3.4.1.1 constant symbols
3.4.1.2 function symbols
3.4.1.3 semantic
3.4.1.4 terms
3.4.1.5 well formed formula (wff)
3.4.1.6 quantifiers
3.4.1.6.1 universal quantifier
3.4.1.6.2 existential quantifier
3.4.1.6.3 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
3.7.1.1. conversion to normal form or clausal form
3.7.1.2 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
5.4.1.1. strategies for exploration of problem space
5.4.1.1.1 data –driven search strategy
5.4.1.1.2 goal-driven search strategy
5.4.1.2 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
5.6.2.1 state representation and initial state
5.6.2.2 operators
5.6.2.3 solution
5.6.2.4 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
5.8.1.1.1 admissibility
5.8.1.1.2 monotonicity or consistency
5.8.1.1.3 informedness
5.8.1.1.4 completeness
5.8.1.1.5 dominance
5.8.1.1.6 optimality
5.8.1.2 heuristic functions
5.8.1.3 hill climbing method and hill climbing search
5.8.1.3.1 disadvantage of hill climbing method
5.8.1.4 simulated annealing
5.8.1.5 best-first search
5.8.1.5.1 disadvantage
5.8.1.6 branch-and-bound search
5.8.1.7 a* search
5.8.1.6.1 properties of a* search
6. game tress or game playing
6.1 introduction to two player games
6.1.1 two player game
6.1.1.1 minimax search
6.1.2 what is nim?
6.1.2.1 computing successor for nim
6.1.2 alpha-beta pruning
6.1.2.1 rules for alpha-beta pruning
6.1.2.2 alpha-beta concept
6.1.2.3 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
7.2.2.1 probability and probability calculus
7.2.2.2. bayes’ theorem and bayesian probabilistic inference
7.2.2.3. bayes’ rule and application of bayes’ rule
7.2.2.4. application of bayes’ rule
7.2.2.5 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
10.14.2.1 feed forward or back propagation model
10.14.2.2 recurrent network
10.14.3 learning or training methods and ann
10.14.3.1 supervised learning
10.14.3.2 unsupervised learning
10.14.3.3 reinforced learning
10.14.4 types of neural networks
10.14.4.1 multilayer perception (mlp)
10.14.4.2 radial basis function networks (rbf)
10.14.4.3 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
12.3.2.1 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
13.1.1.1 syntax
13.1.1.2 semantics
13.1.1.3 pragmatics
13.1.2 parsing
13.1.2.1 context free grammar
13.1.2.2 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
14.2.2.1 fitness function
14.2.2.2 diversity
14.2.2.3 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
15.1.1.1 syntax of fact
15.1.2 rules
15.1.2.1 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
15.4.1.1 write a prolog program to compute fibonacci term using recursion
15.4.1.2 write a prolog program to compute greatest common divisor of two numbers
15.4.1.3 write a prolog program to compute factorial of a positive number using recursion
15.4.1.4 write a prolog program to concatenate two given list
15.4.1.5 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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