A Methodology for Programming Production Systems and its Implications on Parallelism

Production systems have been studied as a language for artificial intelligence programming for over a decade. The flexibility of a programming paradigm which allows for loosely structured, independent rules to represent knowledge is attractive. Unfortunately, two seemingly independent phenomena have hindered the ability to take full advantage of production systems. First, the performance of large production systems suffers due to the large amounts of computation required to run them. Second, the programming styles of individuals primarily accustomed to conventional programming has adversely affected the maintainability and performance of the resulting systems. The parallel execution of production systems has been studied in order to address the performance issues. Preliminary results have been interpreted pessimistically; production systems have been observed to contain only moderate to low levels of parallelism. By investigating the issue of programming style, however, it will be shown that the apparent lack of large-scale or massive parallelism is an artifact of this problem. Indeed, a set of programming guidelines and tools will be presented which yield more maintainable, understandable, and parallelizable production systems. Is there a programming methodology or environment which will allow for the development of more maintainable and parallelizable production systems? This work will attempt to demonstrate that using a combination of several techniques, resulting production systems will more appropriately conform with the theory which supports their use. Production systems are not appropriate for encoding all problem solving tasks. They are appropriate when there is a clear separation of explicit control knowledge, tabular knowledge, and pattern-directed knowledge. This classification has been presented by many researchers in the field, often in order to advocate their separation. The issue has been addressed from a knowledge representation standpoint: here it will be one of several issues which, when addressed properly, will result in systems with improved performance in addition to their more adequate representation of the knowledge. Substantially more paral1elism can be extracted from these systems. In this regard, the techniques complement parallel match algorithms which provide the first step in the solution for mapping production systems onto parallel architectures. The techniques are table-driven rules, creating constrained copies of culprit rules, multiple rule firing, and combining rule chains. These methods are combined into a new way of viewing production system execution. Rather than assuming the sequentiality of production systems and trying to extract parallelism explicitly, the systems are assumed to be implicitly parallel and all necessarily sequential aspects are explicitly defined

Repotting the Geraniums: On Nested Graph Transformation Rules

We propose a scheme for rule amalgamation based on nested graph predicates. Essentially, we extend all the graphs in such a predicate with right hand sides. Whenever such an enriched nested predicate matches (i.e., is satisfied by) a given host graph, this results in many individual match morphisms, and thus many “small” rule applications. The total effect is described by the amalgamated rule. This makes for a smooth, uniform and very powerful amalgamation scheme, which we demonstrate on a number of examples. Among the examples is the following, which we believe to be inexpressible in very few other parallel rule formalism proposed in the literature: repot all flowering geraniums whose pots have cracked.\u

Architectures for reasoning in parallel

The research conducted has dealt with rule-based expert systems. The algorithms that may lead to effective parallelization of them were investigated. Both the forward and backward chained control paradigms were investigated in the course of this work. The best computer architecture for the developed and investigated algorithms has been researched. Two experimental vehicles were developed to facilitate this research. They are Backpac, a parallel backward chained rule-based reasoning system and Datapac, a parallel forward chained rule-based reasoning system. Both systems have been written in Multilisp, a version of Lisp which contains the parallel construct, future. Applying the future function to a function causes the function to become a task parallel to the spawning task. Additionally, Backpac and Datapac have been run on several disparate parallel processors. The machines are an Encore Multimax with 10 processors, the Concert Multiprocessor with 64 processors, and a 32 processor BBN GP1000. Both the Concert and the GP1000 are switch-based machines. The Multimax has all its processors hung off a common bus. All are shared memory machines, but have different schemes for sharing the memory and different locales for the shared memory. The main results of the investigations come from experiments on the 10 processor Encore and the Concert with partitions of 32 or less processors. Additionally, experiments have been run with a stripped down version of EMYCIN

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited

A LISP-Ada connection

The prototype demonstrates the feasibility of using Ada for expert systems and the implementation of an expert-friendly interface which supports knowledge entry. In the Ford LISP-Ada Connection (FLAC) system LISP and Ada are used in ways which complement their respective capabilities. Future investigation will concentrate on the enhancement of the expert knowledge entry/debugging interface and on the issues associated with multitasking and real-time expert systems implementation in Ada

A parallel expert system for the control of a robotic air vehicle

Expert systems can be used to govern the intelligent control of vehicles, for example the Robotic Air Vehicle (RAV). Due to the nature of the RAV system the associated expert system needs to perform in a demanding real-time environment. The use of a parallel processing capability to support the associated expert system's computational requirement is critical in this application. Thus, algorithms for parallel real-time expert systems must be designed, analyzed, and synthesized. The design process incorporates a consideration of the rule-set/face-set size along with representation issues. These issues are looked at in reference to information movement and various inference mechanisms. Also examined is the process involved with transporting the RAV expert system functions from the TI Explorer, where they are implemented in the Automated Reasoning Tool (ART), to the iPSC Hypercube, where the system is synthesized using Concurrent Common LISP (CCLISP). The transformation process for the ART to CCLISP conversion is described. The performance characteristics of the parallel implementation of these expert systems on the iPSC Hypercube are compared to the TI Explorer implementation

Mechanisms for the generation and regulation of sequential behaviour

A critical aspect of much human behaviour is the generation and regulation of sequential activities. Such behaviour is seen in both naturalistic settings such as routine action and language production and laboratory tasks such as serial recall and many reaction time experiments. There are a variety of computational mechanisms that may support the generation and regulation of sequential behaviours, ranging from those underlying Turing machines to those employed by recurrent connectionist networks. This paper surveys a range of such mechanisms, together with a range of empirical phenomena related to human sequential behaviour. It is argued that the empirical phenomena pose difficulties for most sequencing mechanisms, but that converging evidence from behavioural flexibility, error data arising from when the system is stressed or when it is damaged following brain injury, and between-trial effects in reaction time tasks, point to a hybrid symbolic activation-based mechanism for the generation and regulation of sequential behaviour. Some implications of this view for the nature of mental computation are highlighted

Empirical models, rules, and optimization

This paper considers supply decisions by firms in a dynamic setting with adjustment costs and compares the behavior of an optimal control model to that of a rule-based system which relaxes the assumption that agents are explicit optimizers. In our approach, the economic agent uses believably simple rules in coping with complex situations. We estimate rules using an artificially generated sample obtained by running repeated simulations of a dynamic optimal control model of a firm's hiring/firing decisions. We show that (i) agents using heuristics can behave as if they were seeking rationally to maximize their dynamic returns; (ii) the approach requires fewer behavioral assumptions relative to dynamic optimization and the assumptions made are based on economically intuitive theoretical results linking rule adoption to uncertainty; (iii) the approach delineates the domain of applicability of maximization hypotheses and describes the behavior of agents in situations of economic disequilibrium. The approach adopted uses concepts from fuzzy control theory. An agent, instead of optimizing, follows Fuzzy Associative Memory (FAM) rules which, given input and output data, can be estimated and used to approximate any non-linear dynamic process. Empirical results indicate that the fuzzy rule-based system performs extremely well in approximating optimal dynamic behavior in situations with limited noise.Decision-making. ,econometric models ,TMD ,