Production System Language Research Articles

A production system language KDOPS

A production system language KDOPS

A Method of Unified Implementation for Sequential Control by Production Rules.

SFC (Sequential Function Chart) is a kind of graphic representations, which is included in IEC standards for sequential control systems. It is introduced as a specific form of PC's (Programmable Controller's) programs. For the simplicity of graphic representations and the clarity of relations of sequences, SFC is much superior to Ladder Diagram that has been so far, widely used in sequential control systems. Each element of SFC is meaningful just by the connections and SFC is strong in the representation of a flow of control sequence. However, SFC has a weak point that it is difficult to describe logic by conditions in detail like interlock relations and to represent interrupt processing. But production rules are obviously suitable for representing logic by conditions and also for interrupting process.In this paper, we propose a method to translate SFC into production rules, and to implement SFC by production rules using RTPS (Real Time Production System) in which PSL (Production System Language) is used to describe production rules. SFC has three description elements: step representing states in systems, transition representing transition conditions of systems, and link representing the connections between step and transition. We classify the flows of SFC to three types: single sequence, selective sequence and parallel sequence, and, show that each sequence can be described by pairs of transition-step. Conceming a pair of transition-step, a rule for translating the pair of transition-step into production rules is presented. These production rules will be represented by PSL. Finally, we show an example that is implemented by an RTPS.

Knowledge base expert system control of spatial xenon oscillations in pressurized water reactors

Current xenon oscillation control methods used in pressurized water reactors are knowledge intensive, and heuristic in nature. An expert system is developed to implement the heuristic constant axial offset control procedure to aid reactor operators in increasing the plant reliability by reducing the human error component of the failure probability. An expert system is written in the production system language, OPS5, with a forward chaining algorithm. It samples the reactor core with a certain time interval, evaluates the core status to determine the necessary corrective actions in terms of reactivity insertion, and selects the control parameter to realize this reactivity insertion. The amount of control action is determined using a knowledge base which consists of the differential rod worth curves, and the boron reactivity worth of a given reactor. The controller has been tested using a one-dimesional core model for verification of the rules and the code. It has been shown that, having the reactor dependent parameters in its knowledge base, the controller is able to follow a typical load demand for a daily cycle of a reactor, and is able to keep the axial offset within a target band.

An expert system using an exchange heuristic for the resource-constrained scheduling problem

In this paper, we discuss the development and implementation of an expert system for the resource-constrained scheduling problem. The objective of the system is to minimize the makespan (or duration of a project). This approach can accommodate up to 400 operations (activities) and 50 different resources. The knowledge contained in the expert system is based upon an exchange heuristic algorithm that has been applied succesfully to various resource-constrained scheduling problems and has been shown to be a very general and efficient search method for large-sized problems. The exchange heuristic takes an initial schedule and improves upon it via exchanging and rearranging operations, while continually maintaining feasibility. In this system, a forward chaining strategy is used as the inference process. These production system language OPS83 with external C-language links is utilized as the system development tool. In order to evaluate the performance of the expert system, we compared it with an earlier heuristic programming version and found that superior computation performance and enhanced software maintenance were achieved. A number of large-sized problems have been tested on this system, and the results have shown that the makespans were reduced by 10–16 percent compared to initial schedules. Advanced programming techniques were utilized to reduce computer memory requirements and to increase the search speed. Therefore, the system can deal with large-sized problems on PC-compatible machines (with 640KB RAM or more), running MS-DOS, with short CPU execution times.

Timing analysis of MRL: A real-time rule-based system

This paper examines issues on how to predict timing behavior of rule-based decision systems for real-time applications. In particular, we focus on the analysis of response time of rule-based programs written in the production system language MRL. The design goal of MRL is to allow programmers to write OPS5-like rule-based programs in a language that is more amenable to formal analysis based on the semantic foundation underlying the language Unity. The language MRL, its analysis algorithms, and its execution system form a package of design tools for programming real-time rule-based decision systems.

Implementing Backtrack in Production System: A TMS based Approach .

OPS5 is one of the most widely used Production System languages. The control strategies provided in OPS-like languages are extremely weak and consequently during problem solving, frequently dead-ends are encountered. We have made an attempt to perform dependency-directed backtracking within the framework of an OPS-like interpreter which employs a Truth Maintenance System for reasoning with past actions. The conditions for backtrack are characterised by the violation of a set of domain-specific constraints and signaled as contradictions in the Dependency Network (D-Net). We have developed a system, called OPS91, which comprises an enhanced match-select-act cycle operating on a D-Net as working memory. This cycle is explained with reference to the D-Net structure and the correlation between the D-Net and Rete-Net operations. The revision algorithm and null conflict set resolution strategy are detailed. The performance of the system is evaluated and broad guidelines on the programming strategy are presented.

A Production System for AGVS Control

This work present an AGV Expert System (AES) which has been developed to control an automated guided vehicle system. It has been implemented in OPS5, a production system language. The AES structure is shown in two main aspects: the element classes used to model an AGV system and the organization of the AES knowledge base. Finally, rules used to deal with the load assignment problem in systems whose vehicles can transport more than one load each time have been considered.

Expert system approach to detection of epileptiform activity in the EEG.

An expert system for the automated detection of spikes and sharp waves in the EEG has been developed. The system consists of two distinct stages. The first is a feature extractor, written in the conventional procedural language Fortran, which uses parts of previously published spike-detection algorithms to produce a list of all spike-like occurrences in the EEG. The second stage, written in the production system language OPS5, reads the list and uses rules incorporating knowledge elicited from an electroencephalographer (EEGer) to confirm or exclude each of the possible spikes. Information such as the time of occurrence, polarity and channel relationship are used in this process. A summary of the detected epileptiform events is produced which is available to the EEGer in interpreting the EEG. The performance of the expert system is compared with an EEGer using a 320s segment from an EEG containing epileptiform activity. The system detected 19 events and missed seven (false negative) which the EEGer considered epileptiform. There were no false positive detections.

An expert decision support system for assisting power system operators to correct real power flow violations

Abstract This paper presents an expert decision support system which incorporates operator knowledge for correcting real power flow violations in a power system. Details of the implementation are given and the potential of the system is assessed. Rule-based programming techniques are used, the tool being the production system language OPS5+.

MACE: High-level distributed objects in a flexible testbed for distributed AI research

Distributed Artificial Intelligence (DAI) is a subfield of AI concerned with the problems of describing and constructing multiple intelligent systems which interact. MACE (for Multi-Agent Computing Environment) is a language, programming environment, and testbed for DAI systems. Parallelism in AI problem-solving applications can be exploited at many different levels: in hardware, in the implementation language (e.g., a production system language), in a problem-solving paradigm, or directly in the application. MACE is an instrumented testbed for building a wide range of experimental Distributed Artificial Intelligence systems. The goal of MACE is to support experimentation with different styles of distributed AI systems, at different levels of complexity. The dominant metaphor of MACE is a collection of intelligent, semi-autonomous agents interacting in organized ways. MACE is a distributed, object-oriented system. MACE computational units (called agents) run in parallel, and communicate via messages. They provide optional facilities for knowledge representation (world knowledge, models of other agents, their goals and plans, their roles and capabilities, etc.) and reasoning capabilities. The MACE environment maps agents onto processors, handles inter-agent communication, and provides several features: A collection of agents. Agents are the basic computational units of the MACE system. Rather than being simple generic object structures, MACE agents are inherently “social” in nature: they know about some other agents in their environment, and expect to draw upon and coordinate with the expertise of others they know about. MACE agents represent this information in the form of models of other agents in the world in a special database called acquaintances , which is a property of each MACE agent. Current agent models are predicate-calculus based. MACE agents may be composed into sub-units or coalitions; they respond as organized groups or composites. The MACE Agent Description Language includes facilities for describing organized clusters of agents. A community of system agents. A number of pre-defined system agents provide MACE command interpretation, a standard user interface, the agent-builder cluster, error handling, tracing, execution monitoring, etc. Specific MACE agents serve as interactive tools for building and executing MACE programs. A collection of facilities which all agents may use, including a pattern matcher, a simulator, several standard agent engines, handlers for standard errors, and standard messages understood by all agents. MACE incorporates extensive pattern-matching facilities for interpreting messages, for pattern-directed invocation of asynchronous events (e.g., demons and other event-monitors), and for associative database access within agents. Instrumentation. The MACE testbed is instrumented to allow for measurements of the characteristics of problem-solvers during experimental runs. Message traffic, queue and database sizes, work done by an agent (in terms of elapsed real time or number of invocations), and load on a processor node are common measurements. A description database. Agent descriptions are maintained in a description database by a system-agent cluster which constructs new descriptions, verifies descriptions, and constructs executable agents from descriptions. MACE enforces a separation of description and execution to handle some problems of distribution of objects. A collection of kernels. MACE kernels collectively handle communication and message routing, perform I/O to terminals, files, or other devices, map agents onto processors, and schedule agents for execution. Reflection capabilities. A MACE agent's acquaintances database can include, of course, models of the behaviors, plans, roles, or implementation of the agent itself, providing high-level and behavioral reflection capabilities. An evolutionary philosophy. MACE agents are intended to be a high-level approach to distributed object-oriented programming. Since we are using MACE as a testbed to explore coordination strategies for intelligent multi-agent systems, the nature of generic MACE agents changes and specializes over time. All MACE agents now have acquaintance databases, react to certain specialized messages, and include particular attributes (e.g. the attribute “ME” which includes a partial self-description). Over time, as more coordination and modeling features are found useful, they will be standardized as features of MACE agents. In this way, MACE agents are explicitly evolving toward higher-level objects. MACE has been used to build numerous experimental DAI systems including models of lower-level parallelism (sets of distributed production rule systems without global database or inference engine, where each rule is an agent) and higher-level distributed problem-solving architectures (e.g., domain-independent distributed blackboard and contract-net schemes). MACE is implemented on a 16-node INTEL iPSC/1 large-memory hypercube and in a LISP machine environment, and will soon be available on the iPSC/2 and a Sun network.

A self-controlling interpreter for the relational production language

The Relational Production Language (RPL) solves the paradigm mismatch between expert systems and database systems by relying on the relational data model as the underlying formalism for an expert system. The result is a formally-defined production system language with immediate access to conventional databases. Working memory is modeled as a relational database and rules consist of a relational query on the left hand side (LHS) and database updates on the right hand side (RHS). This paper reports on the design of the RPL 1 0 prototype. The prototype directly executes RPL programs and capitalizes on the inherent advantages of the relational approach, particularly for intra-rule and inter-rule parallelism. By using a self-describing approach for representing the interpreter data structures, the interpreter is a self-controlling system that allows conflict resolution, error handling and a wide spectrum of software metrics to be explicitly specified using RPL meta-rules.

A Production System Model for Terrain Analysis Knowledge Representation

A production system model has been developed for terrain analysis problem solving. The working memory of the production system is used to store specific domain knowledge about landforms. This involved the description of landforms in terms of their pattern elements, including their likelihood of occurrence. The production memory was used to store the rules of inferencing. These rules were general rules and were applicable to all landforms. Thus, the domain knowledge about terrain analysis was separated into two components: one component consisting of specific knowledge about landforms, stored as facts in the working memory; and the other component consisting of the general methodology for inferencing, stored as rules in the production memory. Such a separation of knowledge enables additions to the knowledge base fairly easily. The knowledge base can be extended to encompass more landforms simply by creating more working‐memory elements. The present version of the prototype expert system has been implemented using OPSS, a production system language. Uncertainty calculations were performed by invoking LISP functions from OPS5. The results indicated that the production system model was appropriate for designing the prototype expert system for terrain analysis.

A rule-based systems approach to spacecraft communications configuration optimization

An experimental rule-based system for optimizing user spacecraft communications configurations has been developed at NASA to support mission planning for spacecraft that obtain telecommunications services through NASA's Tracking and Data Relay Satellite System. Designated ECCO (Expert for Communications Configuration Optimization), and implemented in the OPS5 production system language, the system has shown the validity of a rule-based systems approach to this optimization problem. This article discusses the development of ECCO and the incremental optimization method on which it is based. A test case using hypothetical mission data is included to demonstrate the optimization concept.

WEAVER: A Knowledge-Based Routing Expert

WEAVER, a channel/switchbox knowledge-based routing program, simultaneously considers all the important routing metrics including 100 percent routability, minimum routing area, minimum wire length, and the minimum number of vias. It allows prerouted nets and user interaction throughout the entire routing process, while relaxing the unnecessary contraints of assigning different directions?constraints imposed by all of the current channel and switchbox routers. A grid-based router using two interconnection layers, WEAVER can be easily expanded to route any shape routing area such as T or +. Implemented in OPSS, a production system language, WEAVER routinely produces routings requiring less area than routers that focus on a single routing metric.

Table-driven rules in expert systems

The structure and organization of expert systems can be usefully modeled after corresponding human experts. Often this modeling degrades because of insufficient expressive power in production system languages. Relational table techniques provide additional abstraction capabilities and are useful in extending the expressiveness of production system rules; the resulting systems can be easier to build, understand and debug because they can reflect more accurately human methods of reasoning. The number of superfluous rules is reduced by organizing much of the problem domain knowledge in relations in working memory. The relational table methods also provide a tool for the interfacing of knowledge bases and databases.

Scope marking in computer conditionals—a psychological evaluation

In a previous paper the authors reported that it was easier for non-programmers to learn to use nested conditional constructions than jumping, or branch-to-label, constructions; however, as only single situations were studied, the conclusions were necessarily restricted. The present study extends the comparison to the more general case where nesting requires “scope markers” to disambiguate the syntax. The results showed that if the scope markers were simply the begin and end of ALGOL 60 (abbreviated NEST-BE) then the advantage of nesting over jumping was weakened; but if the scope markers carried redundant information about the conditional tested (NEST-INE) performance was excellent, particularly at debugging. It seems necessary to distinguish sequence information in a program, which describes the order in which things are done, from taxon information, which describes the conditions under which a given action is performed. Conventional programming languages obscure the taxon information. The advantage of nesting over jumping, we speculate, is in clarifying the sequence information by redundant re-coding in spatial terms; the added advantage of NEST-INE over NEST-BE is that it clarifies the taxon information. It is because debugging requires taxon information that NEST-INE is so much superior. On this view one would expect that in decision table and production system languages, where the taxon information is explicit but the sequence information is obscured, the reverse phenomena should occur. Because debugging requires sequence information as well as taxon information, a device that clarified the sequence would greatly improve such languages.