Search algorithms for finding optimal solutions are, at least from the practical point of view, often enough intractible, so that the search for good ('satisficing') solutions becomes a research topic of its own interest. Satisficing solutions and different approaches to obtain them under various criteria is the subject of these notes, published in the series "Research notes in artificial intelligence". In an introductory chapter the author presents the known point - value and the point - { { set of values} } identification used in search- based decision-algorithms for guiding the search and discusses some of their advantages and disadvantages. This motivates the here studied alternative approach using that evaluation functions do not return a point - value or a range of values corresponding to a point (state) in a tree but now a distribution function, that describes the possible location of the'value' of the state. Chapter 2 introduces this model, Chapter 6 resumes the basic results.

Branch and Bound (B&B) is a problem-solving technique which is widely used for various problems encountered in operations research and combinatorial mathematics. Various heuristic search procedures used in artificial intelligence (AI) are considered to be related to B&B procedures. However, in the absence of any generally accepted terminology for B&B procedures, there have been widely differing opinions regarding the relationships between these procedures and B&B. This paper presents a formulation of B&B general enough to include previous formulations as special cases, and shows how two well-known AI search procedures (A∗ and AO∗) are special cases of this general formulation.

Optical transport networks based on wavelength division multiplexing (WDM) are considered to be the most appropriate choice for future Internet backbone. On the other hand, future DOE networks are expected to have the ability to dynamically provision on-demand survivable services to suit the needs of various high performance scientific applications and remote collaboration. Since a failure in aWDMnetwork such as a cable cut may result in a tremendous amount of data loss, efficient protection of data transport in WDM networks is therefore essential. As the backbone network is moving towards GMPLS/WDM optical networks, the unique requirement to support DOE's sciencemore » mission results in challenging issues that are not directly addressed by existing networking techniques and methodologies. The objectives of this project were to develop cost effective protection and restoration mechanisms based on dedicated path, shared path, preconfigured cycle (p-cycle), and so on, to deal with single failure, dual failure, and shared risk link group (SRLG) failure, under different traffic and resource requirement models; to devise efficient service provisioning algorithms that deal with application specific network resource requirements for both unicast and multicast; to study various aspects of traffic grooming in WDM ring and mesh networks to derive cost effective solutions while meeting application resource and QoS requirements; to design various diverse routing and multi-constrained routing algorithms, considering different traffic models and failure models, for protection and restoration, as well as for service provisioning; to propose and study new optical burst switched architectures and mechanisms for effectively supporting dynamic services; and to integrate research with graduate and undergraduate education.

This paper explores the paradigm that heuristics are discovered by consulting simplified models of the problem domain. After describing the features of typical heuristics on some popular problems, we demonstrate that these heuristics can be obtained by the process of deleting constraints from the original problem and solving the relaxed problem which ensues. We then outline a scheme for generating such heuristics mechanically, which involves systematic refinement and deletion of constraints from the original problem specification until a semidecomposable model is identified. The solution to the latter constitutes a heuristic for the former.

Almost all game tree search procedures used in artificial intelligence are variants on minimaxing. Until recently, it was almost universally believed that searching deeper on the game tree with such procedures would in general yield a better decision. However, recent investigations have revealed the existence of many game trees and evaluation functions which are ‘pathological’ in the sense that searching deeper consistently degrades the decision. This paper extends these investigations in two ways. First, it is shown that whenever the evaluation function satisfies certain properties, pathology will occur on any game tree of high enough constant branching factor.

An extention of the alpha-beta tree pruning strategy to game trees with ‘probability’ nodes, whose values are defined as the (possibly weighted) average of their successors' values, is developed. These ‘*-minimax’ trees pertain to games involving chance but no concealed information. Based upon our search strategy, we formulate and then analyze several algorithms for *-minimax trees. An initial left-to-right depth-first algorithm is developed and shown to reduce the complexity of an exhaustive search strategy by 25–30 percent. An improved algorithm is then formulated to ‘probe’ beneath the chance nodes of ‘regular’ *-minimax trees, where players alternate in making moves with chance events interspersed.

Artificial intelligence 20(4):393-425, July, 1983.

Three experiments have been conducted, and some novel designs and design rules have emerged. The paradigm for Eurisko's exploration is a loop in which it generates a new device configuration, computes its I/O behavior, tries to "parse" this into a functionally it already knows about and can use, and then evaluates the results. In the first experiment, this loop took place at the level of charged carriers moving under the effects of electric fields through abutted regions of doped and undoped semiconductors. This was unsurprising, as they were short sentences in the descriptive language we had defined (a language with verbs like Abut and ApplyEField, and with nouns like nDoped Region and IntrinsicChannellRegion).

Eurisko is an AI program that learns by discovery. We are applying Eurisko to the task of inventing new kinds of three- dimensional microelectronic devices that can then be fabricated using recently developed laser recrystallization techniques. Three experiments have been conducted, and some novel designs and design rules have emerged. The paradigm for Eurisko's exploration is a loop in which it generates a new device configuration, computes its I/O behavior, tries to "parse" this into a functionally it already knows about and can use, and then evaluates the results. In the first experiment, this loop took place at the level of charged carriers moving under the effects of electric fields through abutted regions of doped and undoped semiconductors. Many of the well-known primitive devices were synthesized quickly, such as the MOSFET, Junction Diode, and Bipolar Transistor. This was unsurprising, as they were short sentences in the descriptive language we had defined (a language with verbs like Abut and ApplyEField, and with nouns like nDoped Region and IntrinsicChannellRegion).

In this article we discuss a method for learning useful conditions on the application of operators during heuristic search. Since learning is not attempted until a complete solution path has been found for a problem, credit for correct moves and blame for incorrect moves is easily assigned. We review four learning systems that have incorporated similar techniques to learn in the domains of algebra, symbolic integration, and puzzle-solving. We conclude that the basic approach of learning from solution paths can be applied to any situation in which problems can be solved by sequential search. Finally, we examine some potential difficulties that may arise in more complex domains, and suggest some possible extensions for dealing with them.