Rule-based Production System Research Articles

Knowledge-Based Instrumentation and Control for Competitive Industry-Inspired Robotic Domains

Autonomy is an increasing trend in manufacturing industries. Several industry-inspired robotic competitions have been established in recent years to provide testbeds of comprehensible size. In this paper, we describe a knowledge-based instrumentation and control framework used in several of these competitions. It is implemented using a rule-based production system and creates the task goals for autonomous mobile robots. It controls the environment’s agency using sensor data from processing stations and instructs proper reactions. The monitoring and collection of various data allows for an effective instrumentation of the competitions for evaluation purposes. The goal is to achieve automated runs with no or as little human intervention as possible which would allow for more and longer lasting runs. It provides a general framework adaptable to suit many scenarios and is an interesting test case for knowledge-based systems in an industry-inspired setting.

Rule-based runtime verification revisited

Runtime verification (RV) consists in part of checking execution traces against user-provided formalized specifications. Throughout the last decade many new systems have emerged, most of which support specification notations based on state machines, regular expressions, temporal logic, or grammars. The field of artificial intelligence (AI) has for an even longer period of time studied rule-based production systems, which at a closer look appear to be relevant for RV, although seemingly focused on slightly different application domains, such as, for example, business processes and expert systems. The core algorithm in many of these systems is the Rete algorithm. We have implemented a rule-based system, named LogFire, for runtime verification, founded on the Rete algorithm, as an internal DSL in the Scala programming language (in essence a library). Using Scala's support for defining DSLs allows to write rules elegantly as part of Scala programs. This combination appears attractive from a practical point of view. Our contribution is part conceptual in arguing that such rule-based frameworks originating from AI are suited for RV. Our contribution is technical by implementing an internal rule DSL in Scala; by illustrating how specification patterns can easily be encoded that generate rules, and by adapting and optimizing the Rete algorithm for RV purposes. An experimental evaluation is performed comparing to six other trace analysis systems. LogFire is currently being used to process telemetry from the Mars Curiosity rover at NASA's Jet Propulsion Laboratory.

An adaptive genetic-based signature learning system for intrusion detection

Rule-based intrusion detection systems generally rely on hand crafted signatures developed by domain experts. This could lead to a delay in updating the signature bases and potentially compromising the security of protected systems. In this paper, we present a biologically-inspired computational approach to dynamically and adaptively learn signatures for network intrusion detection using a supervised learning classifier system. The classifier is an online and incremental parallel production rule-based system. A signature extraction system is developed that adaptively extracts signatures to the knowledge base as they are discovered by the classifier. The signature extraction algorithm is augmented by introducing new generalisation operators that minimise overlap and conflict between signatures. Mechanisms are provided to adapt main algorithm parameters to deal with online noisy and imbalanced class data. Our approach is hybrid in that signatures for both intrusive and normal behaviours are learnt. The performance of the developed systems is evaluated with a publicly available intrusion detection dataset and results are presented that show the effectiveness of the proposed system.

Artificial Intelligence: Structures and Strategies for Complex Problem Solving

Artificial Intelligence: Structures and Strategies for Complex Problem Solving by George F. Luger 6th edition, Addison Wesley, 2008 The book serves as a good introductory textbook for artificial intelligence, particularly for undergraduate level. It covers major AI topics and makes good connection between different areas of artificial intelligence. Along with each technique and algorithm introduced in the book, is a discussion of its complexity and application domain. There is an attached website to the book that provides auxiliary materials for some chapters, sample problems with solutions, and ideas for student projects. Besides Prolog and Lisp, java and C++ are also used to implement many of the algorithms in the book. The book is organized in five parts. The first part (chapter 1) gives an overview of AI, its history and its various application areas. The second part (chapters 2–6) concerns with knowledge representation and search algorithms. Chapter 2 introduces predicate calculus as a mathematical tool for representing AI problems. The state space search as well as un-informed and heuristic search methods is introduced in chapters 3 and 4. Chapter 5 discusses the issue of uncertainty in problem solving and covers the foundation of stochastic methodology and its application. In chapter 6 the implementation of search algorithms is shown in production system and blackboard architectures. Part 3 (chapters 7–9) discusses knowledge representation and different methods of problem solving, including strong, weak and distributed problem solving. Chapter 7 begins with reviewing the history of evolution of AI representation schemes, including semantic networks, frames, scripts and conceptual graphs. This chapter ends with a brief introduction of Agent problem solving. Chapter 8 presents the production model and rule-based expert systems as well as case-based and model-based reasoning. The methods of dealing with various aspects of uncertainty are discussed in chapter 9. These methods include Dempster-Shafer theory of evidence, Bayesian and Belief networks, fuzzy logics and Markov models. Part 4 is devoted to machine learning. Chapter 10 describes algorithms for symbol-based learning, including induction, concept learning, vision-space search and ID3. The neural network methods for learning, such as back propagation, competitive, Associative memories and Hebbian Coincidence learning were presented in chapter 11. Genetic algorithms and evolutionary learning approaches are introduced in chapter 12. Chapter 13 introduces stochastic and dynamic models of learning along with Hidden Markov Models, Dynamic Baysian networks and Markov Decision Processes. Part 5 (chapters 14 and 15) examines two main application of AI: automated reasoning and natural language understanding. Chapter 14 begins with an introduction to weak methods in problem solving and continues with presenting resolution theorem proving. Chapter 15 deals with the complex issue of natural language understanding by discussing main methods of syntax and semantic analysis of natural language corpus. The chapter ends with examples of natural language application in Database query generation, text summarization and question answering systems. Finally, chapter 16 is a summary of the materials covered in the book as well current AI's limitations and future directions. One criticism about the book would be that the materials are not covered in enough depth. Because of the space limitation, many important AI algorithms and techniques are discussed briefly without providing enough details. As a result, some chapters (e.g., 8, 9, 11, and 13) of the book should be supported by complementary materials to make it understandable for undergraduate students and motivating for graduate students. Another issue is with the structure of the book. The order of presenting chapters introduces sequentially different challenges and techniques in problem solving. Consequently, some topics such as uncertainty and logic are not introduced separately and are distributed in different chapters of the book related to different parts. Although interesting, this makes the book hard to follow. In summary, the book gives a great insight to the readers that want to familiar themselves with artificial intelligence. It covers a broad range of topics in AI problem solving and its practical application and is a good reference for an undergraduate level introductory AI class. Elham S. Khorasani, Department of Computer Science Southern Illinois University Carbondale, IL 62901, USA

Automation of strip-layout design for sheet metal work on progressive die

Abstract Strip-layout design is an important step in the planning stage of sheet metal work on progressive die. It is an experience-driven activity and the quality of strip-layout is highly dependent on the knowledge and skill of die designers. This paper presents an expert system for automation of strip-layout design process. The proposed system is developed using the production rule-based expert system approach of Artificial Intelligence (AI). It comprises six modules to impart expert advices to the user for identifying sheet metal operations, sequencing of operations, selection of proper piloting scheme, number of stations, staging of operations on progressive die and selection of proper dimensions of stock strip. Finally, the system models the strip-layout automatically in the drawing editor of AutoCAD using the output data files of other modules. Usefulness of the system is demonstrated through an example of an industrial component. The system is flexible and has low cost of implementation.

Prioritization of congenital cardiac surgical patients using fuzzy reasoning – a solution to the problem of the waiting list?

Surgical waiting lists are of high importance in countries, where the national health system is unable to deliver surgical services at a rate that would allow patients to avoid unnecessary periods of waiting. Prioritization of these lists, however, is frequently arbitrary and inconsistent. The objective of our research was to analyze the medical decision-making process when prioritizing patients with congenital cardiac malformations for cardiac surgical procedures, identifying an appropriate representation of knowledge, and transferring this knowledge onto the design and implementation of an expert system ("PrioHeart"). The medical decision-making process was stratified into three stages. The first was to analyze the details of the procedure and patient to define important impact factors on clinical priority, such as the risk of adverse events. The second step was to evaluate these impact factors to define an appropriate "timing category" within which a procedure should be performed. The third, and final, step was to re-evaluate the characteristics of individual patients to differentiate between those in the same timing category. We implemented this decision-making process using a rule-based production system with support for fuzzy sets, using the FuzzyCLIPS inference engine and expert system shell as a suitable development environment for the knowledge base. The "PrioHeart" expert system was developed to give paediatric cardiologists a tool to allow and facilitate the prioritization of patients on the cardiosurgical waiting list. Evaluation of "PrioHeart" on limited sets of patients documented appropriate results of prioritization, with a significant correlation between the prioritization made using "PrioHeart" and those results obtained by the individual consultant specialist. We conclude that our study has demonstrated the feasibility of using an expert system approach with a fuzzy, rule-based production system to implement the prioritization of cardiac surgical patients. The approach may potentially be transferable to other medical subspecialities.

Knowledge-based search in competitive domains

Artificial intelligence programs operating in competitive domains typically use brute-force search if the domain can be modeled using a search tree or alternately use nonsearch heuristics as in production rule-based expert systems. While brute-force techniques have recently proven to be a viable method for modeling domains with smaller search spaces, such as checkers and chess, the same techniques cannot succeed in more complex domains, such as shogi or go. This research uses a cognitive-based modeling strategy to develop a heuristic search technique based on cognitive thought processes with minimal domain specific knowledge. The cognitive-based search technique provides a significant reduction in search space complexity and, furthermore, enables the search paradigms to be extended to domains that are not typically thought of as search domains such as aerial combat or corporate takeovers.

Fuzzy reasoning Petri nets

This paper presents a fuzzy reasoning Petri net (FRPN) model to represent a fuzzy production rule-based system. The issues of how to represent and reason about rules containing negative literals are addressed in the proposed PN model. The execution rules based on the model are defined formally using the operators in max-algebra. Then, a fuzzy reasoning algorithm is proposed to perform fuzzy reasoning automatically. The algorithm is consistent with the matrix equation expression method in the traditional PNs and allows one to exploit the maximum parallel reasoning potential embedded in the model. The legitimacy and feasibility of the proposed approach are proved and validated through a turbine fault diagnosis expert system.

Designing reusable rule-based architectures with design patterns

Abstract Rule-based systems or production systems still have great importance in the construction of knowledge systems. In these systems, the domain expertise to solve a problem is encoded in the form of ‘if–then’ rules, enabling a modular description of the knowledge, thus facilitating its maintenance and updating. Although they have been extensively described in the Artificial Intelligence literature, their design process is at times repeated because of the lack of common software architecture and the restrictions offered by some off-the-shelf libraries and systems. This paper proposes a reusable architecture for rule-based systems described through design patterns. The aim of these patterns is to constitute a design catalog that can be used by designers to understand and create new rule-based systems, thus promoting reuse in these systems. Additionally, the use of the described patterns in the design of an intelligent tutoring system architecture is exemplified.

Genetic production systems for intelligent problem solving

The paper discusses an evolutionary knowledge approach to intelligent problem solving. A rule-based production system is used to model the problem and the means by which the problem space should be searched. Search heuristics are modelled as production rules. These rules are redundant as there may be more than one view on the best method for building solutions. Some rules may have complex reasoning for their actions, others have none. Deciding which rule is most appropriate is solved by a genetic algorithm and ultimately only the ‘fitter’ rules will survive. The approach eliminates the necessity of designing problem specific search or variation operators, leaving the genetic algorithm to process patterns independent of the problem at hand. Learning methods and how they aid evolution is also discussed: they are Lamarckian learning and the Baldwin effect. The approach is tested on a scheduling problem.

Bucket brigade algorithm for hierarchical censored production rule-based system

A bucket brigade algorithm for production rule-based system is reviewed and its suitability to variable precision logic-based systems is explored. A modified version of the bucket brigade suitable for a censored production rule-based system and hierarchical censored production rule-based system is proposed. The standard bucket brigade algorithm is modified by appending rule status to each rule in addition to its strength. The rule status (unknown, correct, not correct, doubtful, or confirmed) helps in deciding the strategy for bid payment. The proposed algorithm incorporates strategy to resolve the problem of weakening of correct rules which are activated by incorrect rules. The problem is resolved by continuously weakening such incorrect rules, thereby not allowing them to enter the conflict set. Examples are included to illustrate various ideas involved in the proposed algorithm. © 1996 John Wiley & Sons, Inc.

Adaptable neuro production systems

Connectionist production systems are neural network realizations of production rule-based systems. The connections are adjusted to a given set of rules to allow the system to perform reasoning. Adaptable connectionist production systems are introduced in this paper. They allow adaptation of the already pre-calculated connections to new data. The production rules are used to initialize the connection weights after which training with data occurs. At any time of the neural network operation, a set of updated rules can be extracted as a current knowledge base accumulated by the network. Using a set of rules for initializing a connectionist architecture before training may result in: (1) increase in the speed of training; (2) increase in the robustness of the neural network against the ‘catastrophic forgetting’ phenomenon; (3) better explanation of the learned by the network knowledge from data. In general, the proposed method facilitates building flexible and adaptable neuro-fuzzy production systems. This is demonstrated on a case problem of chaotic time series prediction.

Encoding of Production Rules in a Connectionist Network

The task of implementing a simple rule-based production system in a connectionist architecture is discussed. To implement a connectionist-based production system, it is essential to be capable of representing explicit rules by means of simple neuronlike computing units. The architecture described here uses a local representation based upon the instance oriented symbolic/connectionist expert system SC net. The problems of variable binding and variable value instantiation are solved in the context of this system. The network is built such that only one value is bound to a variable during each rule firing cycle. Issues regarding serialization and working memory consistency are also solved. An example implementation, the Farmer's Dilemma planning problem, is given to illustrate the potential of this approach. The article shows how many different rule bases may be mapped onto a connectionist architecture for parallel execution.

Formal semantics for rule-based systems

The existence of a formal description of any language is a prerequisite to any rigorous methods of proof, validation, or verification. This article develops a formal semantics in the denotational style for rule-based production systems. Primarily, a formal description of a generalized, hypothetical language for rule-based expert systems is developed as a base. Building on this base, it is possible to specify in detail the variations that distinguish individual real-world systems in a usable way.

A semantics for a class of non-deterministic and causal production system programs

We define a class of function-free rule-based production system (PS) programs that exhibit non-deterministic and/or causal behavior. We develop a fixpoint semantics and an equivalent declarative semantics for these programs. The criterion to recognize the class of non-deterministic causal (NDC) PS programs is based upon extending and relaxing the concept of stratification, to partition the rules of the program. Unlike strict stratification, this relaxed stratification criterion allows a more flexible partitioning of the rules and admits programs whose execution is non-deterministic or causal or both. The fixpoint semantics is based upon a monotonic fixpoint operator which guarantees that the execution of the program will terminate. Each fixpoint corresponds to a minimal database of answers for the NDC PS program. Since the execution of the program is non-deterministic, several fixpoints may be obtained. To obtain a declarative meaning for the PS program, we associate a normal logic program\(\overline {PS} \) with each NDC PS program. We use the generalized disjunctive well-founded semantics to provide a meaning to the normal logic program\(\overline {PS} \) Through these semantics, a well-founded state is associated with\(\overline {PS} \) and a set of possible extensions, each of which are minimal models for the well-founded state, are obtained. We show that the fixpoint semantics for the NDC PS programs is sound and complete with respect to the declarative semantics for the corresponding normal logic program\(\overline {PS} \).

Coupling production systems and database systems: a homogeneous approach

Methods for storing and manipulating large rule bases using a relational database management systems (DBMS) are discussed. An approach to decomposing and storing the condition elements in the antecedents of rules such as those used in production rule-based systems is presented. A set-oriented approach, DBCond, which uses a special data structure that is implemented using relations is proposed. A matching algorithm for DBCond uses the relational structures to efficiently identify rules whose antecedents are satisfied. The performance of DBCond is compared with that of DBRete, a DBMS implementation of the Rete match algorithm developed for use with the production rule language OPS5. DBCond is also compared with DBQuery, a method that is based on evaluating queries corresponding to the conditions in the antecedents of the rules. Improvements to the data structure and the algorithms of the DBCond method are described. An advantage of DBCond is that it is fully parallelizable, thus making it attractive for parallel computing environments. >

Development of a self-tuning fuzzy logic controller

In developing a Fuzzy Logic Controller, the establishment of boundaries between adjacent fuzzy sets of the function that characterizes these boundaries is a formidable task. Considerable re-examination of the various process state combinations is necessary to ensure that the output distribution changes in a reliable and consistent manner. Rule-based production systems that depend on fuzzy logic can produce vacuums and valleys in the output distributions. These deficiencies must be eliminated. An Expert System has been developed to adjust the boundary conditions of a two-variable fuzzy logic control system using a feedback learning procedure. Membership values are recalculated on each cycle to ensure smooth transfer of output belief from one rule to another as the process states change. This method is performed whenever the confidence level of the definition of a specific linguistic expression is adjusted. The application of this controller to supervice a secondary crushing plant simulator is described.

GTSIM A computer simulation of music perception

GTSIM is a computer simulation model of human perception of rhythm, based on Lerdahl and Jackendoff'sA Generative Theory of Tonal Music. GTSIM, a rule-based model with a neural network component, simulates the metric and grouping stages of Lerdahl and Jackendoff's theory. Using a rule-based production system, GTSIM simulates parallel processing, operating left-to-right without backtracking. GTSIM identifies the key of the piece of music and produces hierarchical representations of the metric and grouping structure which conform to Lerdahl and Jackendoff.

A structured methodology for developing production systems

Issues such as project management, documentation, and maintenance, typically associated with large traditional sofware development projects have recently become important concerns in the development of expert systems. We describe a structured methodology for the development of production rule-based expert systems. The methodology prescribes a procedure to help knowledge engineers manage the complexity of a knowledge base by viewing rule and parameter dependencies at successive levels of abstraction. Parameters are clustered into modules, in manner analogous to functional decomposition in structured design. The modularization thus obtained provides three significant benefits: ease of subsequent knowledge base maintenance, enhancement of code reusability, and support for knowledge base verification. The visual tools used by the methodology facilitate communication between knowledge engineers and experts and provide ongoing documentation of the system.

Modeling Pilot Expertise in Air Combat

The purpose of this effort was to model expert pilot performance and decision making in one-versus-one (1v1) air-to-air combat. Several knowledge-elicitation techniques were used to extract air combat expertise from a former fighter pilot, who served as the subject-matter-expert (SME). Unstructured and then structured interviews were used to elicit the goals and sub-goals of air-to-air combat, plus some of the pilot behaviors necessary to accomplish the goals. The SME also flew a number of combat sorties against another former fighter pilot in the Simulator for Air-to-Air Combat (SAAC) to demonstrate pilot performance required to accomplish the goals of air combat. Based on the SME's verbal protocols, a group of air combat rules were developed. A rule-based production system was then designed to incorporate the resulting knowledge base. The production system was also designed to be capable of analyzing an existing data base of air combat engagements. Expert system development required additional input from the SME to identify specific values of flight parameters required by the production system. Upon completion and SME verification of the expert model, it will be validated by comparing its performance to that of our SME in simulated air-to-air combat. If the model can successfully describe expert pilot performance, the model will be used to provide diagnostic performance feedback in conjunction with SAAC training.