Mathematical Aspects of Artificial Intelligence

Examines the connections between Artificial Intelligence (AI) and mathematics, demonstrating the potential for mathematical applications and exposing some of the more mathematical areas within AI. This book discusses mathematical techniques involving graph theory, combinatorics and probability and statistics.

There exists a history of great expectations and large investments involving Artificial Intelligence (AI). There are also notable shortfalls and memorable disappointments. One major controversy regarding AI is just how mathematical a field it is or should be. This text includes contributions that examine the connections between AI and mathematics, demonstrating the potential for mathematical applications and exposing some of the more mathematical areas within AI.The goal is to stimulate interest in people who can contribute to the field or use its results. Included is work by M. Newborn on the famous Deep Blue chess match. He discusses highly mathematical techniques involving graph theory, combinatorics and probability and statistics. G. Shafer offers his development of probability through probability trees with some of the results appearing here for the first time. M. Golumbic treats temporal reasoning with ties to the famous Frame Problem.His contribution involves logic, combinatorics and graph theory and leads to two chapters with logical themes. H. Kirchner explains how ordering techniques in automated reasoning systems make deduction more efficient. Constraint logic programming is discussed by C. Lassez, who shows its intimate ties to linear programming with crucial theorems going back to Fourier. V. Nalwa''s work provides a brief tour of computer vision, tying it to mathematics-from combinatorics, probability and geometry to partial differential equations. All authors are gifted expositors and are current contributors to the field. The wide scope of the volume includes research problems, research tools and good motivational material for teaching.