Review of Heuristics: Intelligent Search Strategies for Computer Problem Solving

AI Magazine

The view of AI science offered by Judea Pearl is thoroughly traditional and standard, and therein lie both this book's strengths and its weaknesses as a monograph, a reference, or a textbook.





Evaluating Influence Diagrams

Classics

See also: Evaluating influence diagrams with decision circuits. In Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI2007)Operations Research, 34, 871-882


Explanation-Based Generalization: A Unifying View

Classics

"The problem of formulating general concepts from specific training examples has long been a major focus of machine learning research. While most previous research has focused on empirical methods for generalizing from a large number of training examples using no domain-specific knowledge, in the past few years new methods have been developed for applying domain-specific knowledge to formulate valid generalizations from single training examples. The characteristic common to these methods is that their ability to generalize from a single example follows from their ability to explain why the training example is a member of the concept being learned. This paper proposes a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization. The EBG method is illustrated in the context of several example problems, and used to contrast several existing systems for explanation-based generalization. The perspective on explanation-based generalization afforded by this general method is also used to identify open research problems in this area." Machine Learning, 1 (1), 47–80.


Real-time obstacle avoidance for robot manipulator andmobile robots

Classics

This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the artificial potential field concept. Collision avoidance, tradi tionally considered a high level planning problem, can be effectively distributed between different levels of control, al lowing real-time robot operations in a complex environment. This method has been extended to moving obstacles by using a time-varying artificial patential field. We have applied this obstacle avoidance scheme to robot arm mechanisms and have used a new approach to the general problem of real-time manipulator control. We reformulated the manipulator con trol problem as direct control of manipulator motion in oper ational space--the space in which the task is originally described--rather than as control of the task's corresponding joint space motion obtained only after geometric and kine matic transformation.