ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS FOR ENGINEERS

By E. CAIRN

FUNDAMENTALS CHAPTER INTRODUCTION CHAPTER INTRODUCTION TO BAYESIAN NETWORKS AND INFLUENCE DIAGRAMS AN INTRODUCTION TO FULLY AND PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES AN INTRODUCTION TO REINFORCEMENT LEARNING SECTION CONCEPTS CHAPTER INFERENCE STRATEGIES FOR SOLVING SEMI-MARKOV DECISION PROCESSES MULTISTAGE STOCHASTIC PROGRAMMING: A SCENARIO TREE BASED APPROACH TO PLANNING UNDER UNCERTAINTY AUTOMATICALLY GENERATED EXPLANATIONS FOR MARKOV DECISION PROCESSES DYNAMIC LIMIDS RELATIONAL REPRESENTATIONS AND TRACES FOR EFFICIENT REINFORCEMENT LEARNING SOLUTIONS CHAPTER A DECISION-THEORETIC TUTOR FOR ANALOGICAL PROBLEM SOLVING DYNAMIC DECISION NETWORKS APPLICATIONS IN ACTIVE LEARNING SIMULATORS AN INTELLIGENT ASSISTANT FOR POWER PLANT OPERATION AND TRAINING BASED ON DECISION- THEORETIC PLANNING POMDP MODELS FOR ASSISTIVE TECHNOLOGY A CASE STUDY OF APPLYING DECISION THEORY IN THE REAL WORLD: POMDPS AND SPOKEN DIALOG SYSTEMS TASK COORDINATION FOR SERVICE ROBOTS BASED ON MULTIPLE MARKOV DECISION PROCESSES APPLICATIONS OF DEC-MDPS IN MULTI-ROBOT SYSTEMS COMPILATION OF REFERENCES