Chapter 1 Introduction
1.1 The underlying Assumptions
1.2 AI Technique
1.3 Level of the model
1.4 Criteria for Success
1.5 The problem as a State Space Search
Chapter 2 Production Systems
2.1 Problem characteristics
2.2 Production system characteristics
2.3 Issues in the design of search programs
Chapter 3 Agents and environments
3.1 The nature of environments
3.2 Properties of task environments
3.3 Design the structure of agents
3.4 Hill climbing, Best – first search
3.5 Problem reduction
3.6 Constraint Satisfaction
3.7 Means-Ends Analysis (Current, GOAL)
3.8 Representations and mappings
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Chapter 4 Mapping between facts and representations
4.1 Approaches to knowledge Representation
4.2 Issues in knowledge representation, while framing problem
4.3 Simple facts in logic, instance and ISA relationship
4.4 Computable functions and Natural Deduction
4.5 Knowledge based agents and the Wumpus world
4.6 Propositional Logic, Propositional Theorem Proving
4.7 Agents based on propositional logic
4.8 Logic for non-monotonic reasoning
Chapter 5 Dempster-Shafer theory
5.1 Fuzzy logic with details
5.2 Implementation Issues
5.3 Augmenting a problem-solver
5.4 Implementing Depthfirst search
Chapter 6 Bayesian Network
6.1 All events, and various probabilities
6.2 An example to apply Bayes Theorem
Chapter 7 Certainity factors and rule-based systems
7.1 Acting under uncertainity
7.2 Basic Probability notations and Example
7.3 Wumpus world revisited
7.4 Semantic Nets and Semantic Frames
7.5 Implementing Optimal Decision in games
7.6 The minimax algorithm
7.7 Alpha-Beta Pruning
7.8 Imperfect Real time Decisions
7.9 Evaluation Functions
Chapter 8 Stochastic Games, particularly observable game
8.1 Examples for state of the art game programs
8.2 Need for alternative approaches
8.3 Forms of learning, supervised learning, decision trees
8.4 Evaluation and choosing the best hypothesis
8.5 The theory of learning, PAC, regression
8.6 Non parametric models, support vector machines
8.7 Support vector machine
8.8 Statistical learning with complete data
8.9 Learning with hidden variables
8.10 Implementation of the EM algorithm
8.11 Handwritten digit recognition
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