A semantics for a class of stratified production system programs

Data & Knowledge Engineering, 1997

Production rules are useful for several tasks of active database systems, such as integrity constraint checking, derived data maintenance, database state monitoring, etc. Furthermore production rules can express knowledge in a high-level form for problem solving in Knowledge Base Systems (KBS). Present active object-oriented database (OODB) systems traditionally provide event-driven rules which are triggered by events, i.e. database modifications. This paper describes DEVICE, a high-level rule integration scheme into an active OODB system, resulting in an active KBS. The paper emphasises on the run-time processing of production rules, namely the incremental matching of rule conditions, as well as rule selection and firing. The matching of production rules requires special algorithms based on the flow of updated data through a discrimination network, like RETE, TREAT, etc. DEVICE offers a smooth integration of production rules into an active OODB system that only supports event-driven rules, without introducing new data structures, maintaining at the same time the properties of discrimination networks. This is achieved using complex events to map the conditions of production rules and monitor the database to incrementally match those conditions. DEVICE maps each production rule into one event-driven rule that is easy to maintain and offers centralised rule selection control for correct run-time behaviour and conflict resolution. Furthermore, DEVICE provides the infrastructure for the integration of various other rule paradigms into a single KBS, like deductive rules and integrity constraints and leaves room for the optimisation of the matching process through variations of the basic discrimination network.

Dke, 1997

Production rules are useful for several tasks of active database systems, such as integrity constraint checking, derived data maintenance, database state monitoring, etc. Furthermore production rules can express knowledge in a high-level form for problem solving in Knowledge Base Systems (KBS). Present active object-oriented database (OODB) systems traditionally provide event-driven rules which are triggered by events, i.e. database modifications. This paper describes DEVICE, a high-level rule integration scheme into an active OODB system, resulting in an active KBS. The paper emphasises on the run-time processing of production rules, namely the incremental matching of rule conditions, as well as rule selection and firing. The matching of production rules requires special algorithms based on the flow of updated data through a discrimination network, like RETE, TREAT, etc. DEVICE offers a smooth integration of production rules into an active OODB system that only supports event-driven rules, without introducing new data structures, maintaining at the same time the properties of discrimination networks. This is achieved using complex events to map the conditions of production rules and monitor the database to incrementally match those conditions. DEVICE maps each production rule into one event-driven rule that is easy to maintain and offers centralised rule selection control for correct run-time behaviour and conflict resolution. Furthermore, DEVICE provides the infrastructure for the integration of various other rule paradigms into a single KBS, like deductive rules and integrity constraints and leaves room for the optimisation of the matching process through variations of the basic discrimination network.

2009

Production rules and logic programs can be combined in a single logic-based framework. The framework gives both an operational and modeltheoretic semantics to production rules, as well as to logic programs extended with a database of facts that is modified by destructive assignment. The model-theoretic semantics is obtained by separating the production system working memory into facts and goals. Logic programs are used to define ramifications of the facts and to reduce goals to sub-goals, including actions. The execution of actions generates a sequence of states, which serves as a candidate model of the production rules.

The aim of this research is to develop a model for designing rule-based expert systems that uses the forward chaining method of inference. The striking aspect of this model is that the inference engine is based on a simple representation of rules and facts in relational database tables. Rules are decomposed and represented in tables at two levels, which allow the developing of expert systems in any programming language that supports SQL. The explanation facility uses tables containing the explanations of the result of each rule. The model proposed in this paper is based on a simple approach to represent facts and rules in relational database tables. The advantage of this model lies in focusing the design of rule-based expert systems toward knowledge representation in a relational database, reducing effort and programming difficulties.

1985

The paper presents a novel expert system architecture which supports explicit representation and effective use of both declarative and procedural knowledge. These two types of expert knowledge are represented by means of produc tion rules and event-graphs respectively, and they are pro cessed by a unified inference engine. Communication between the rule level and the event-graph level is based on a full visibility of each level on the internal state of the other, and it is structured in such a way as to allow each level to exert control on the other. This structure offers several advantages over more tradi tional architectures. Knowledge representation is more natural and transparent; knowledge acquisition turns out to be easier as pieces of knowledge can be immediately represented without the need of complex transformation and restructuring; inference is more effective due to reduced non-determinism resulting from explicit represen tation of fragments of procedural knowledge in eventgraphs; finally, explanations are more natural and under standable. The proposed architecture has been adopted for the design of PROP, an expert system for on-line monitoring of the cycle water pollution in a thermal power plant. PROP is run ning on a SUN-2 workstation and has been tested on a sam ple of real cases.

1996

Selecting a suitable method and knowledge engineering tool for a rule-based problem may be influenced by the fact that an expert system shell can be expensive and, contrary to database systems, may not be part of the installed computing environment. We show how to use a database (ORACLE) to implement the functionality of an expert system shell by a simple rule language which is automatically translated into SQL. The SQL interpreter is then used to "execute" these rules. We have applied this method to process control, in particular to checking timing sequences in the CERN particle accelerator complex. Our experience gained during more than one year of use, showed that the initial limitations concerning the execution speed could be overcome by software optimisation and hardware upgrades. Finally we compare the inference engine of this rule system implemented in a database with that of standard production rule expert system shells.

Expert Systems with Applications, 2018

There is a rich body of work dedicated to expert systems. However, none of them focus on control strategies suitable for distributed environments. We describe a novel approach to design distributed expert systems that is able to control rule activation; it involves control strategies supporting selective inferencing with rules that propagate concurrently and incorporates a set of metarules that operate on a blackboard and that are expressed as an assurance case. It is implemented on top of ERESYE, a tool for the realization of expert systems that is written in Erlang. We describe an implementation of the approach in an industrial setting, using an example focusing on the feature identification problem, whose resolution is an important task for source code maintenance and evolution.

Design and implementation of distributed expert systems: On a control strategy to manage the execution flow of rule activation, 2018

There is a rich body of work dedicated to expert systems. However, none of them focus on control strategies suitable for distributed environments. We describe a novel approach to design distributed expert systems that is able to control rule activation; it involves control strategies supporting selective inferencing with rules that propagate concurrently and incorporates a set of meta-rules that operate on a blackboard and that are expressed as an assurance case. It is implemented on top of ERESYE, a tool for the realization of expert systems that is written in Erlang. We describe an implementation of the approach in an industrial setting, using an example focusing on the feature identification problem, whose resolution is an important task for source code maintenance and evolution.

The Knowledge Engineering Review, 1993

Database researchers have recognized that integrating a production rules facility into a database system provides a uniform mechanism for a number of advanced database features including integrity constraint enforcement, derived data maintenance, triggers, protection, version control, and others. In addition, a database system with rule processing capabilities provides a useful platform for large and efficient knowledge-base and expert systems. Database systems with production rules are referred to asactive database systems, and the field of active database systems has indeed been active. This paper summarizes current work in active database systems, and suggests future research directions. Topics covered include database rule languages, rule processing semantics, and implementation issues.

We briefly describe the logic programming language PROLOG concentrating on those aspects of the language that make it suitable for implementing expert systems. We show how features of expert systems such as: (1) inference generated requests for data, (2) probabilistic reasoning, (3) explanation of behaviour can be easily programmed in PROLOG. We illustrate each of these features by showing how a fault finder expert could be programmed in PROLOG.