A Methodology for Developing Correct Rule-Based Programs for Parallel Implementation

Production systems, also called rule-based systems, are very useful in automating certain human expert tasks, but the current technology exhibits many problems. We believe that parallelism is difficult to exploit in production system programs for two reasons. First, the original serial programs are designed with a priori knowledge of an explicit global control mechanism which must be simulated for correct execution in parallel. The second reason for the difficulty is that no formal language exists in which to express these programs and no verification techniques are utilized to prove properties which guarantee correct execution in parallel. With these two obstacles removed, a correct rule-based program can be designed to exploit increased parallelism when mapped to a parallel production system model for execution. This research will concentrate on the development of parallel production system programs. The objective is to define a theoretical foundation to describe parallel production systems for implementation in parallel architectures. The Swarm language will be used as the vehicle for encoding the programs. Swarm\u27s associated proof theory will be used, and possibly extended, to show correctness of properties necessary to guarantee correct parallel execution of the rule-based programs. Thus, the overall contribution of this research will be a methodology for defining, developing, and encoding correct parallel production system programs

A Survey of Three Applications of Parallelism in AI

Once the BEMAS [13] system was completed and recorded in Common Lisp, research efforts were channeled toward three primary areas. This report will present a briefly review of some research in these areas, which are: parallelizing truth maintenance systems, parallelizing production systems, and parallel search. The area of parallel search has been studied by many over the past years and we will only present current research that has been accomplished. This review represents the beginning research into the development of a parallel inference model

Correct Parallel Status Assignments for the Reason Maintenance System

This paper represents a beginning development of a parallel truth maintenance system to interact with a parallel inference engine. We present a solution which performs status assignments in parallel to belief nodes in the Reason Maintenance System (RMS) presented by [3],[4]. We examine a previously described algorithms by [7] which fails to correctly detect termination of the status assignments. Under Petrie\u27s algorithm, termination may go undetected an in certain circumstances (namely the existence of an unsatisfiable circularity) a false detection may occur. We present an algorithm that corrects these problems

Belief Maintenance Systems: Initial Prototype Specification

A fundamental need in future information systems is an effective method of accurately representing and monitoring dynamic, real-world situations inside a computer. Information is represented using an Extended Open World Assumption (EOWA), in which the data are explicitly true or false. Reasoning within the EOWA is done through the use of a dynamic dependency net which only represents those beliefs and justifications that are both currently valid and in current use. In this paper, we present definitions and uses of the EOWA and dynamic dependency net in our current research of developing a database with which we can use deductive reasoning with limited resources. A prototype has been implemented for determining the existing problems of creating such a belief management system for operation in real-world applications

BEMAS: User\u27s Manual, 2nd Edition

This paper is a user\u27s manual for BEMAS, a BE lief MA intenance System. BEMAS is a menu driven system which provides an easy to use interface between a user and a knowledge base. Given a set of data, and a set of rules, BEMAS will help the user to identify an object by analyzing the properties of that object. Data can be added and deleted at any time, either directly or by deleting beliefs on which the data is dependent. BEMAS maintains the relations and dependencies between data using a dynamic dependency net. BEMAS also has the capability to make inferences using incomplete information while still maintaining knowledge base integrity

BEMAS: A Belief Maintenance System Prototype User\u27s Manual

This paper is a user\u27s manual for BEMAS, a Belief Maintenance System. BEMAS is a menu driven system which provides an easy to use interface between a user and a knowledge base. Given a set of data, and a set of rules, BEMAS will help the user to identify an object by analyzing the properties of that object. Data can be added and deleted at any time, either directly or by deleting beliefs on which the data is dependent. BEMAS maintains the relations and dependencies between data using a dynamic dependency net. BEMAS also has the capability to make inferences using incomplete information while still maintaining knowledge base integrity

Asymmetric magnetization reversal in exchange biased polycrystalline F/AF bilayers

This paper describes a model for magnetization reversal in polycrystalline Ferromagnetic/Antiferromagnetic exchange biased bilayers. We assume that the exchange energy can be expanded into cosine power series. We show that it is possible to fit experimental asymmetric shape of hysteresis loops in exchange biased bilayer for any direction of the applied field. The hysteresis asymmetry is discussed in terms of energy considerations. An angle beta is introduced to quantify the easy axis dispersion of AF grains.Comment: 15 pages, 4 figure

NBS monograph

From Abstract: "This report describes the hardware required and discusses practical ways in which the necessary circuitry can be built into prime equipment.

Towards a Fully Parallel Reason Maintenance System

A truth maintenance system (TMS) is an AI system used to monitor consistency of information in a knowledge base. A TMS may be necessary when non-monotonic reasoning is used since incorrect assumptions can lead to contradictory conclusions. The Reason Maintenance System (RMS), a specific TMS first described by Doyle [5],[6], is used along with an inference engine (IE), or problem solver, to maintain a consistent set of beliefs and inferences. We have developed a parallel version of the RMS for correctly assigning IN or OUT states to each believe node [7]. This algorithm uses diffusing computation [4] to assign the status to a node. In this paper we will further parallelize the RMS by superimposing a locking mechanism on the RMS to have simultaneous status assignment computations performed. Also, we will address how contradiction handling can be executed in parallel and the effect on the RMS when a parallel contradiction handler is incorporated