This accessible, comprehensive book captures the essence of artificial intelligence — solving the complex problems that arise wherever computer technology is applied. With his signature enthusiasm, George Luger demonstrates numerous techniques and strategies for addressing the many challenges facing computer scientists today. Diverse topics on this exciting and ever-evolving field range from perception and adaptation using neural networks and genetic algorithms, intelligent agents with ontologies, automated reasoning, natural language analysis, and stochastic approaches to machine learning.
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Chapter 1 introduces artificial intelligence, beginning with a brief history of attempts to understand mind and intelligence in philosophy, psychology, and other areas of research. In an important sense, AI is an old science, tracing its roots back at least to Aristotle. An appreciation of this background is essential for an understanding of the issues addressed in modern research. We also present an overview of some of the important application areas in AI. Our goal in Chapter 1 is to provide both background and a motivation for the theory and applications that follow.
Chapters 2, 3, 4, 5, and 6 (Part II) introduce the research tools for AI problem solving. These include, in Chapter 2, the predicate calculus presented both as a mathemat- ical system as well as a representation language to describe the essential features of a problem. Search, and the algorithms and data structures used to implement search, are introduced in Chapter 3, to organize the exploration of problem situations. In Chapter 4, we discuss the essential role of heuristics in focusing and constraining search-based prob- lem solving. In Chapter 5, we introduce the stochastic methodology, important technology for reasoning in situations of uncertainty. In Chapter 6, we present a number of software architectures, including the blackboard and production system, for implementing these search algorithms.
Chapters 7, 8, and 9 make up Part III: representations for AI, knowledge-inten- sive problem solving, and reasoning in changing and ambiguous situations. In Chap- ter 7 we present the evolving story of AI representational schemes. We begin with a discussion of association-based networks and extend this model to include conceptual dependency theory, frames, and scripts. We then present an in-depth examination of a par- ticular formalism, conceptual graphs, emphasizing the epistemological issues involved in representing knowledge and showing how these issues are addressed in a modern repre- sentation language. Expanding on this formalism in Chapter 14, we show how conceptual graphs can be used to implement a natural language database front end. We conclude Chapter 7 with more modern approaches to representation, including Copycat and agent- oriented architectures.
Chapter 8 presents the rule-based expert system along with case-based and model- based reasoning, including examples from the NASA space program. These approaches to problem solving are presented as a natural evolution of the material in Part II: using a pro- duction system of predicate calculus expressions to orchestrate a graph search. We end with an analysis of the strengths and weaknesses of each of these approaches to knowl- edge-intensive problem solving.
Chapter 9 presents models for reasoning with uncertainty as well as the use of unreli- able information. We introduce Bayesian models, belief networks, Dempster-Shafer, causal models, and the Stanford certainty algebra for reasoning in uncertain situations.
Chapter 9 also contains algorithms for truth maintenance, reasoning with minimum mod- els, logic-based abduction, and the clique-tree algorithm for Bayesian belief networks.
Part IV, Chapters 10 through 13, is an extensive presentation of issues in machine learning. In Chapter 10 we offer a detailed look at algorithms for symbol-based learning, a fruitful area of research spawning a number of different problems and solution approaches. These learning algorithms vary in their goals, the training data considered, their learning strategies, and the knowledge representations they employ. Symbol-based learning includes induction, concept learning, version-space search, and ID3. The role of inductive bias is considered, generalizations from patterns of data, as well as the effective use of knowledge to learn from a single example in explanation-based learning. Category learning, or conceptual clustering, is presented with unsupervised learning. Reinforcement learning, or the ability to integrate feedback from the environment into a policy for mak- ing new decisions concludes the chapter.
In Chapter 11 we present neural networks, often referred to as sub-symbolic or con- nectionist models of learning. In a neural net, information is implicit in the organization and weights on a set of connected processors, and learning involves a re-arrangement and modification of the overall weighting of nodes and structure of the system. We present a number of connectionist architectures, including perceptron learning, backpropagation, and counterpropagation. We demonstrate Kohonen, Grossberg, and Hebbian models. We present associative learning as well as attractor models, including examples of Hopfield networks.
Genetic algorithms and evolutionary approaches to learning are introduced in Chapter 12. On this viewpoint, learning is cast as an emerging and adaptive process. After several examples of problem solutions based on genetic algorithms, we introduce the application of genetic techniques to more general problem solvers. These include classifier systems and genetic programming. We then describe society-based learning with examples from artificial life, called a-life, research. We conclude the chapter with an example of emergent computation from research at the Santa Fe Institute.
Chapter 13 presents stochastic approaches to machine learning. We begin with a defi- nition of hidden markov models and then present several important variations including the auto-regressive and hierarchical HMM. We then present dynamic Bayesian networks, a generalization of the HMM, and also able to track systems across periods of time. These techniques are useful for modeling the changes in complex environments as is required for diagnostic and prognostic reasoning. Finally, we add a probabilistic component to rein- forcement learning first introduced in Chapter 10. This includes presentation of the Markov decision process (or MDP) and the partially observed Markov decision process (or POMDP).
Part V, Chapters 14 and 15, presents automated reasoning and natural language understanding. Theorem proving, often referred to as automated reasoning, is one of the oldest areas of AI research. In Chapter 14, we discuss the first programs in this area, including the Logic Theorist and the General Problem Solver. The primary focus of the chapter is binary resolution proof procedures, especially resolution refutations. More advanced inferencing with hyper-resolution and paramodulation is also presented. Finally, we describe the Prolog interpreter as a Horn clause and resolution-based inferencing sys- tem, and see Prolog computing as an instance of the logic programming paradigm.
Chapter 15 presents natural language understanding. Our traditional approach to lan- guage understanding, exemplified by many of the semantic structures presented in Chap- ter 7, is complemented with the stochastic approach. These include using Markov models, CART trees, CHART parsing (the Earley algorithm), mutual information clustering, and statistics-based parsing. The chapter concludes with examples applying natural language techniques to database query generation, a text summarization systems well as.the use of machine learning to generalize extracted results from the WWW.
Finally, Chapter 16 serves as an epilogue for the book. It addresses the issue of the possibility of a science of intelligent systems, and considers contemporary challenges to AI; it discusses AI's current limitations, and projects its exciting future.