Production Rule System Research Articles

A Heuristic Algorithm for Isolated Obstacle Detection by a Mobile Robot Based on Ranging Data

An algorithm for single isolated obstacle detection by a mobile robot using a range finder is described. The main algorithm block is constructed as a system of production rules that introduce logical relationships that make it possible to determine whether there is an obstacle in the field of normals to the surface. Detected obstacles are plotted on a 2D map. Obstacle mapping methods are discussed.

INTELLIGENT CONTROL SYSTEM OF CHEMICAL-TECHNOLOGICAL PROCESSES OF STRUCTURING OF MULTICOMPONENT ELASTOMER COMPOSITES BASED ON THE PRODUCTION MODEL

A systemic analysis of the life cycle of products from elastomeric materials was carried out. The main modules of a management system for managing the life cycle - development, production and distribution of products from elastomeric composites on the basis of the integrated information system are described. The main control points of the process control are indicated. A detailed description of complex chemical-technological processes of multicomponent elastomeric composites structuring from the positions of a system approach based on a complex of information and production models is proposed. The model of an automated control system for the chemicaltechnological processes of elastomeric composites structuring based on vibration-rheometric data is considered, which is an example of the use of a control system based on indirect indicators. It is shown that the disturbing effects leading to the deviation of the process indices from the given ones are of a prescription and technological nature. The architecture of the integrated information management system for complex chemical-technological processes of multicomponent elastomeric composites mixing and structuring based on the production rules system is proposed. A block diagram of the algorithm for processing the main rheometric information of elastomeric composites structuring is given.

DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS

The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics’ changes over time (e.g., due to aging). The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

Introduction to the special issue on the International Web Rule Symposia 2012–2014

The annual International Web Rule Symposium (RuleML) is an international conference on research, applications, languages, and standards for rule technologies. It has evolved from an annual series of international workshops since 2002, international conferences in 2005 and 2006, and international symposia since 2007. It is the flagship event of the Rule Markup and Modeling Initiative (RuleML, http://ruleml.org), a nonprofit umbrella organization of several technical groups from academia, industry, and government working on rule technology and its applications. RuleML is the leading conference to build bridges between academia and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rule-based systems including production rules systems, logic programming rule engines, and business rules engines/business rules management systems; Semantic Web rule languages and rule standards (e.g., RuleML, SWRL, RIF, PRR, SBVR, DMN, CL, Prolog); rule-based event processing languages and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules.

An event calculus production rule system for reasoning in dynamic and uncertain domains

AbstractAction languages have emerged as an important field of knowledge representation for reasoning about change and causality in dynamic domains. This paper presents Cerbere, a production system designed to perform online causal, temporal and epistemic reasoning based on the Event Calculus. The framework implements the declarative semantics of the underlying logic theories in a forward-chaining rule-based reasoning system, coupling the high expressiveness of its formalisms with the efficiency of rule-based systems. To illustrate its applicability, we present both the modeling of benchmark problems in the field, as well as its utilization in the challenging domain of smart spaces. A hybrid framework that combines logic-based with probabilistic reasoning has been developed, that aims to accommodate activity recognition and monitoring tasks in smart spaces.

A Report on the Ninth International Web Rule Symposium

The annual International Web Rule Symposium (RuleML) is an international conference on research, applications, languages, and standards for rule technologies. RuleML is a leading conference to build bridges between academe and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule‐based programming and rule‐based systems including production rules systems, logic programming rule engines, and business rule engines/business rule management systems; semantic web rule languages and rule standards; rule‐based event‐processing languages (EPLs) and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules. The Ninth International Web Rule Symposium (RuleML 2015) was held in Berlin, Germany, August 2–5. This report summarizes the events of that conference.

Multidimensional Fuzzy Control System Development of Oil Transportation and Treatment Technological Processes Based on the Input/Output Parameters Model in the Precise Terms Set Form

As pumps and pumping stations of oil collecting, maintaining reservoir pressure, transportation and treatment processes account for over 50 % of oil and gas enterprises estimated total cost of electricity, relevant issue is the development and application of intelligent control systems for energy efficiency of these processes. For the booster pump station as a multivariable nonlinear control object are proposed: a structure in which the function of regulating valves transferred to the pumps with frequency speed control; conceptual model with seven inputs and five outputs (adjustable) variables, which is the basis for constructing three-dimensional fuzzy controller with not-fuzzy terms to stabilize and improve the energy efficiency of oil transportation and treatment processes and minimize energy losses. In this paper we propose a control system of pumping station on the basis of multidimensional fuzzy logic controller with compensation of the mutual influence of control loops in the form of an additional system of production rules that helped to stabilize the liquid level at 2.5 m given level with an absolute error of ± 0.2 m and thus improve quality oil emulsion on the installation of oil and gas refining central collection point.

Intellectual Control of Oil and Gas Transportation System by Multidimensional Fuzzy Controllers with Precise Terms

In order to improve the pump station control quality a special case of fuzzy controllers has been suggested to use, in which both the input and output variables are represented by a set of mutually non-overlapping terms of the rectangular shape of the membership function (precise terms). As it has been found out there is only one term in this set at any moment of time that is equal to a logical unit, whereas in the production rules system operating such terms, at any arbitrary moment of time, only one production rule antecedent is equal to a logical unit. This allows without losing of the control adequacy in each controller production system scanning cycle not to execute it completely, but solely as far as the rule, whose antecedent at a given moment is equal the logical unit, which also offers immense opportunities to increase the controller speed. In fuzzy controllers with precise terms, unlike the conventional fuzzy controllers the control precision has been shown to be invariant with respect to the complexity of the production rules conditional part structure. It is now clear that precision of fuzzy controllers with precise terms in the control systems as represented by verbal models is compatible with PID-controllers precision while using them to control the linear process facilities.

Production method of describing automated controllers in the analysis of continuous-discrete control systems

A production method for the description and analysis of continuous-discrete control systems is outlined. A mathematical model of hybrid continuous-discrete control systems is considered. The continuous component of the system (the model of the object of control) is represented by differential equations. The discrete component of the system, which simulates the control device, consists of its operational algorithm in the form of a production rule system (a set of if-then rules). The production method is a universal approach to describing a broad spectrum of controllers. The benefits of this approach are demonstrated, both in the description of digital controllers and in the analysis of continuous-discrete control systems.

Extension of methods of intelligent control of complex objects

It was determined that the existing methods and models do not fully implement the object control strategy. The research problem statement was formulated as the control function optimization on a set of restrictions. The need for modifying the existing models was shown. The modified model as a system of fuzzy production rules, which unlike the existing ones, expands the functional capabilities and improves the accuracy of intelligent object control, was implemented. An ultra-fast annealing method that guarantees only a statistical finding of the global minimum was considered. A modification of the method, which greatly improves the quality of intelligent control by multiple findings of local optima at different initial approximations, was proposed. The performed simulation experiments confirmed the effectiveness of the obtained solutions. The prospects for further studies were defined.

Exchanging Conflict Resolution in an Adaptable Implementation of ACT-R

AbstractIn computational cognitive science, the cognitive architecture ACT-R is very popular. It describes a model of cognition that is amenable to computer implementation, paving the way for computational psychology. Its underlying psychological theory has been investigated in many psychological experiments, but ACT-R lacks a formal definition of its underlying concepts from a mathematical-computational point of view. Although the canonical implementation of ACT-R is now modularized, this production rule system is still hard to adapt and extend in central components like the conflict resolution mechanism (which decides which of the applicable rules to apply next).In this work, we present a concise implementation of ACT-R based on Constraint Handling Rules which has been derived from a formalization in prior work. To show the adaptability of our approach, we implement several different conflict resolution mechanisms discussed in the ACT-R literature. This results in the first implementation of one such mechanism. For the other mechanisms, we empirically evaluate if our implementation matches the results of reference implementations of ACT-R.

E-servant: an intelligent, programmable system to support and integrate assisted living technologies.

The 'E-servant', a programmable system to control and manage assistive technologies, telehealth and telecare devices in a home environment is presented. The E-servant is programmed using a simple graphical interface that allows the user to build a dialogue in the form of a production rule system, which is triggered by a patient- or technology-initiated event. The patient interacts with the system through a personalised user interface to reach their goal of completing a task. These tasks, which the authors call 'scenarios', can be designed for users of different abilities (cognitive and/or physical). They can also be given priority levels, for example if a potential emergency situation arises in the patient's home, a scenario associated with the sensing of this event takes highest priority. The research presented in this Letter outlines the E-servant, its programming tool and reports its evaluation in living laboratory settings. The results suggest that it can be used as a central management system for supporting an integrated support environment for facilitating healthcare and activities of daily living, especially for older patients.

Inference of Probabilistic Grammars in Different Rules Systems of Natural Languages

Abstract Grammar induction is an important and current area within computational linguistics. The results in induction of string transformation rules can be applied not only in linguistics but in many other pattern recognition tasks. The task of grammar induction belongs in general to NP -hard problems. The paper analyses the main rule types in natural languages and in string transformation systems. The paper focuses on the inference of inflectional rule systems which differs in many aspects from the traditional production rule system of Chomsky grammars. The work compares the main candidate methods on the learning of objective-case in the Hungarian language.

How Do Agents Make Decisions? A Survey

When designing an agent-based simulation, an important question to answer is how to model the decision making processes of the agents in the system. A large number of agent decision making models can be found in the literature, each inspired by different aims and research questions. In this paper we provide a review of 14 agent decision making architectures that have attracted interest. They range from production-rule systems to psychologically- and neurologically-inspired approaches. For each of the architectures we give an overview of its design, highlight research questions that have been answered with its help and outline the reasons for the choice of the decision making model provided by the originators. Our goal is to provide guidelines about what kind of agent decision making model, with which level of simplicity or complexity, to use for which kind of research question.

Live multilingual thinking machine

The Extended Hierarchical Censored Production Rules (EHCPRs) system is presented here as an underlying methodology for representation, reasoning, learning, etc., of the proposed live multilingual thinking machine. Enriched representation scheme is a very important aspect and central to all the successful and effective Artificial Intelligence systems. An EHCPR is presented here as a unit of represented knowledge (i.e. an artificial neuron) in the knowledge treasure distributed over globally spread servers. An EHCPR is implemented with the help of extensive set of predefined pointers like dendrites in a neuron and various nodes like nucleus in a neuron, which would span a huge but efficient multilingual hierarchical network as knowledge structure. The EHCPRs system is started live at www.live-ehcprs-system.com with multilingual representation and storage at word level only. But construction of words is by starting at character level without repetition in representation of a character. It displays the stored information of any stored concept in any of the selected language. To begin with, English, French and Hindi are offered to the user which would be extended to all the languages in future.

Special issue on advances in Learning Classifier Systems

Learning Classifier Systems (LCS) constitute a uniquely adaptable class of learning framework existing at the intersection of machine learning and evolutionary computation. Fundamentally, an LCS combines genetic search with an appropriate learning strategy to evolve a rule set which collectively describes a temporal or spatial problem. Since their conceptualization, a variety of algorithmic architectures and mechanisms have been introduced with the objective of improved functionality or application to new problem domains. As technology advances, the power and versatility of LCS algorithms makes them increasingly appealing. Since 1992, the International Workshop on Learning Classifier Systems (IWLCS) has sought to promote the ongoing development of LCS architecture, application, and theory. Beginning in 2003, the IWLCS has been held annually, in association with the Genetic and Evolutionary Computation Conference (GECCO). This special issue includes original works as well as selected and revised papers from the 13th and 14th IWLCS, held in Portland, USA, during GECCO 2010, and Dublin, IR, during GECCO 2011. Over the last few years, there has been tremendous progress in (1) the systematic design of novel LCSs that can deal with additional problem topologies, (2) theoretical analyses incorporated to better understand and improve existing systems and (3) applications within important domains. The works collected in this special issue extend this trend, illustrating the maturity of LCSs and their applicability to hard real-world problems that currently elude solution. In Production System Rules as Protein Complexes from Genetic Regulatory Networks: An Initial Study, Bull presents a new type of production system rules, by introducing a new, indirect encoding that views rules as protein complexes produced by the temporal behavior of an artificial genetic regulatory network. The experimental analysis shows that the new innovative approach has potential, and opens up different lines for future research. Taking a look back to the origins of LCSs, in Risk Neutrality in Learning Classifier Systems Smith develops a novel model of risk-neutral reinforcement learning in a traditional Bucket Brigade credit-allocation market which is adjusted by a genetic algorithm. The work revises two important aspects in LCSs: default hierarchies and long chains of coupled classifiers. The new model is extensively discussed, reviving some ideas present in the first LCSs proposals by Holland. In Analysing BioHEL Using Challenging Boolean Functions, the issue moves to different systematic analysis of current LCSs that enable a better understanding of the systems and give light to future improvements. In this paper, Franco et al. examine how BioHEL, a hierarchical evolutionary learning system specifically designed to cope with large-scale datasets, behaves on a family of Boolean functions whose complexity is varied along different dimensions. The experimental analysis shows the D. Loiacono Politecnico di Milano, P.zza Leonardo Da Vinci, 32, 20133 Milan, Italy e-mail: loiacono@elet.polimi.it

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems.

A Knowledge Representation and Learning Model Based on FNPN in PAAIS

Through used Fuzzy Petri Net to set up the knowledge representation and reasoning rules, a Fuzzy Neural Petri Network (FNPN) method whose parameters can be trained by Neural Network was proposed in this paper. The experimental result indicated that, the Neural Network and the Petri net combination can make the fuzzy production rule systems more effective, and also can be applied to most practical Petri net models and fuzzy systems.

Dynamical Genetic Programming in XCSF

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to artificial neural networks. This paper presents results from an investigation into using a temporally dynamic symbolic representation within the XCSF learning classifier system. In particular, dynamical arithmetic networks are used to represent the traditional condition-action production system rules to solve continuous-valued reinforcement learning problems and to perform symbolic regression, finding competitive performance with traditional genetic programming on a number of composite polynomial tasks. In addition, the network outputs are later repeatedly sampled at varying temporal intervals to perform multistep-ahead predictions of a financial time series.

Production system rules as protein complexes from genetic regulatory networks: an initial study

This short paper introduces a new way by which to design production system rules. An indirect encoding scheme is presented which views such rules as protein complexes produced by the temporal behaviour of an artificial genetic regulatory network. This initial study begins by using a simple Boolean regulatory network to produce traditional ternary-encoded rules before moving to a fuzzy variant to produce real-valued rules. Competitive performance is shown with related genetic regulatory networks and rule-based systems on benchmark problems.