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

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

Visual Data Exploration and Analysis - Report on the Visualization Breakout Session of the SCaLeS Workshop

Scientific visualization is the transformation of abstract information into images, and it plays an integral role in the scientific process by facilitating insight into observed or simulated phenomena. Visualization as a discipline spans many research areas from computer science, cognitive psychology and even art. Yet the most successful visualization applications are created when close synergistic interactions with domain scientists are part of the algorithmic design and implementation process, leading to visual representations with clear scientific meaning. Visualization is used to explore, to debug, to gain understanding, and as an analysis tool. Visualization is literally everywhere--images are present in this report, on television, on the web, in books and magazines--the common theme is the ability to present information visually that is rapidly assimilated by human observers, and transformed into understanding or insight. As an indispensable part a modern science laboratory, visualization is akin to the biologist's microscope or the electrical engineer's oscilloscope. Whereas the microscope is limited to small specimens or use of optics to focus light, the power of scientific visualization is virtually limitless: visualization provides the means to examine data that can be at galactic or atomic scales, or at any size in between. Unlike the traditional scientific tools for visual inspection, visualization offers the means to ''see the unseeable.'' Trends in demographics or changes in levels of atmospheric CO{sub 2} as a function of greenhouse gas emissions are familiar examples of such unseeable phenomena. Over time, visualization techniques evolve in response to scientific need. Each scientific discipline has its ''own language,'' verbal and visual, used for communication. The visual language for depicting electrical circuits is much different than the visual language for depicting theoretical molecules or trends in the stock market. There is no ''one visualization too'' that can serve as a panacea for all science disciplines. Instead, visualization researchers work hand in hand with domain scientists as part of the scientific research process to define, create, adapt and refine software that ''speaks the visual language'' of each scientific domain