Expert systems in the simulation domain

Expert systems are operational in several application areas where expertise is essential in tackling practical problems. Simulation of systems also requires a lot of expert's knowledge and skill. Therefore, expert systems could also successfully be applied in the field of simulation. In this paper the possibilities of this new and powerful simulation tool are discussed.     

Fuzzy arithmetic-based interpolative reasoning for nonlinear dynamic fuzzy systems

FAIR (fuzzy arithmetic-based interpolative reasoning)—a fuzzy reasoning scheme based on fuzzy arithmetic, is presented here. Linguistic rules of the Mamdani type, with fuzzy numbers as consequents, are used in an inference mechanism similar to that of a Takagi–Sugeno model. The inference result is a weighted sum of fuzzy numbers, calculated by means of the extension principle. Both fuzzy and crisp inputs and outputs can be used, and the chaining of rule bases is supported without increasing the spread of the output fuzzy sets in each step. This provides a setting for modeling dynamic fuzzy systems using fuzzy recursion. The matching in the rule antecedents is done by means of a compatibility measure that can be selected to suit the application at hand. Different compatibility measures can be used for different antecedent variables, and reasoning with sparse rule bases is supported. The application of FAIR to the modeling of a nonlinear dynamic system based on a combination of knowledge-driven and data-driven approaches is presented as an example.     

Compositional reasoning about responsive systems with limited resources

A compositional network proof theory to specify and verify properties of fault-tolerant real-time distributed systems with limited resources is presented. In this theory a conceptual scheduler grants the resource using on-line preemptive priority scheduling where the priority is a function of the initial priority and the time spent waiting for the resource. The method enables reasoning about responsive systems which must respond to external inputs in a timely, dependable, and predictable manner. It allows us to abstract from the precise nature and occurrence of faults and to focus on how they affect the externally visible input and output behaviour. To this end a failure hypothesis is formalized as a relation between the system's normal behaviour (i.e., the behaviour when no faults occur) and its acceptable behaviour, that is, the normal behaviour together with the exceptional behaviour (i.e., the behaviour whose abnormality should be tolerated). The proof theory is compositional to allow reasoning with the specifications of processes while ignoring their implementation details.Supported by the Dutch STW under grant number NWI88.1517: Fault Tolerance: Paradigms, Models, Logics, Construction.     

Expert systems for manufacturing cell simulation and design

Computer Integrated Manufacturing (CIM) is approached by means of the application of Computer Aided Design (CAD), Computer Aided Manufacturing (CAM) and other CA techniques, methods and programs/program systems. These programs are often implemented as knowledge-based, or expert systems and in this way they became typical examples of engineering application of artificial intelligence. The production task of CIM systems is solved by using flexible manufacturing systems (FMSs). FMSs built up from smaller, complex units, i.e. from flexible manufacturing cells (FMCs) have several advantages. The design and the operation of manufacturing systems need new, sophisticated methods to utilize all the embedded benefits of the sophisticated and expensive elements installed for production purposes. New methods like knowledge processing technology, cooperative problem-solving techniques, etc., offer wide possibilities to design more reasonable systems. This paper describes prototype expert systems that make use of different knowledge-based tools and techniques to design (configure, reconfigure) and simulate manufacturing cells, taking into consideration technological plans and other relevant information.     

A study of a dynamic progressive reasoning system

Controlling resource-bounded systems not only involves taking decisions about planning and scheduling reasoning components to achieve time-constrained goals, but also about how to monitor the progress of these components. We propose an extended closed-loop planning, scheduling and execution. This extended closed-loop allows one to address the following limitations: (1) dynamic and time-constrained environments, (2) uncertainty regarding the duration of reasoning components and (3) tolerance to execution failure. The interactions in the extended closed-loop allow one: first to revise the current schedule when deviations from a predetermined schedule occur during execution, second to update continuously the set of goals on which the control focuses its decision and third how this set of goals is updated when an execution failure occurs. We discuss these issues in a progressive reasoning system implemented and applied to a railway control application where real-time (dynamic and time-constrained) situations occur.     

Expert system for simulation of metal sheet stamping

Stamping processes are frequently used in the automotive industry. In an attempt to reduce developing times and costs, conventional design and manufacturing procedures are being changed. Finite element simulations have proved to be a good help in the design and analysis of these processes. The main problem of these simulations is that they are quite difficult to perform and that there are several non-trivial questions that the user has to answer before achieving a good model. In this work, a new application has been designed. It combines the usability of a custom application with the power of such a tool as the finite element method. To design this application, a lot of questions regarding FEM simulation of stamping processes have been analyzed. The result is a methodology to automate simulations of stamping processes where the user does not need to have deep knowledge of the finite element software. Such methodology has been successfully employed in a Spanish manufacturing industry of automotive components.     

A generic complete dynamic logic for reasoning about purity and effects

For a number of programming languages, among them Eiffel, C, Java, and Ruby, Hoare-style logics and dynamic logics have been developed. In these logics, pre- and postconditions are typically formulated using potentially effectful programs. In order to ensure that these pre- and postconditions behave like logical formulae (that is, enjoy some kind of referential transparency), a notion of purity is needed. Here, we introduce a generic framework for reasoning about purity and effects. Effects are modelled abstractly and axiomatically, using Moggi’s idea of encapsulation of effects as monads. We introduce a dynamic logic (from which, as usual, a Hoare logic can be derived) whose logical formulae are pure programs in a strong sense. We formulate a set of proof rules for this logic, and prove it to be complete with respect to a categorical semantics. Using dynamic logic, we then develop a relaxed notion of purity which allows for observationally neutral effects such writing on newly allocated memory.     

Quantified epistemic logics for reasoning about knowledge in multi-agent systems

We introduce quantified interpreted systems, a semantics to reason about knowledge in multi-agent systems in a first-order setting. Quantified interpreted systems may be used to interpret a variety of first-order modal epistemic languages with global and local terms, quantifiers, and individual and distributed knowledge operators for the agents in the system. We define first-order modal axiomatisations for different settings, and show that they are sound and complete with respect to the corresponding semantical classes.The expressibility potential of the formalism is explored by analysing two MAS scenarios: an infinite version of the muddy children problem, a typical epistemic puzzle, and a version of the battlefield game. Furthermore, we apply the theoretical results here presented to the analysis of message passing systems [R. Fagin, J. Halpern, Y. Moses, M. Vardi, Reasoning about Knowledge, MIT Press, 1995; L. Lamport, Time, clocks, and the ordering of events in a distributed system, Communication of the ACM 21 (7) (1978) 558–565], and compare the results obtained to their propositional counterparts. By doing so we find that key known meta-theorems of the propositional case can be expressed as validities on the corresponding class of quantified interpreted systems.     

Influence-based model decomposition for reasoning about spatially distributed physical systems

Many important science and engineering applications, such as regulating the temperature distribution over a semiconductor wafer and controlling the noise from a photocopy machine, require interpreting distributed data and designing decentralized controllers for spatially distributed systems. Developing effective computational techniques for representing and reasoning about these systems, which are usually modeled with partial differential equations (PDEs), is one of the major challenge problems for qualitative and spatial reasoning research.

This paper introduces a novel approach to decentralized control design, influence-based model decomposition, and applies it in the context of thermal regulation. Influence-based model decomposition uses a decentralized model, called an influence graph, as a key data abstraction representing influences of controls on distributed physical fields. It serves as the basis for novel algorithms for control placement and parameter design for distributed systems with large numbers of coupled variables. These algorithms exploit physical knowledge of locality, linear superposability, and continuity, encapsulated in influence graphs representing dependencies of field nodes on control nodes. The control placement design algorithms utilize influence graphs to decompose a problem domain so as to decouple the resulting regions. The decentralized control parameter optimization algorithms utilize influence graphs to efficiently evaluate thermal fields and to explicitly trade off computation, communication, and control quality. By leveraging the physical knowledge encapsulated in influence graphs, these control design algorithms are more efficient than standard techniques, and produce designs explainable in terms of problem structures.     

High-speed simulation of discrete dynamic probabilistic systems

The theory and design of a special purpose stochastic computer for the high-speed simulation of Markov chains and random walks is described. Experimental results are presented for the transient and steady response to Markov systems and for fundamental studies of random walks. The paper conlcludes with a discussion of the extension of the system to the Monte-Carlo solution of partial differential equations with arbitrary boundary conditions.     

Stability of hybrid simulation of dynamic systems

This note analyses the problem of stability of hybrid simulation of dynamic systems. Time delays and sample-and-hold operations do cause serious stability problems, and if frequently happens that considerable time and effort are wasted because instability is not known in advance of programming the simulation on the hybrid computer. However, if the system under investigation can be approximated in its response by an equivalent linear system, then it is possible to define a procedure which will predict in advance whether any proposed computing scheme is stable. The text describes one such procedure, together with its implementation as a software package which can be used to predict the stability of simulations of practical size. In addition, the text contains a number of examples which illustrate the application of the software package.     

Improving dynamic domain reduction test data generation method by Euler/Venn reasoning system

Software Quality Journal - Test data adequacy is a major challenge in software testing literature. The difficulty is to provide sufficient test data to assure the correctness of the program under...     

实现机器人动态路径规划的仿真系统

针对机器人动态路径规划问题,提出了在动态环境中移动机器人的一种路径规划方法,适用于环境中同时存在已知和未知,静止和运动障碍物的复杂情况。采用栅格法建立机器人空间模型,整个系统由全局路径规划和局部避碰规划两部分组成。在全局路径规划中,用快速搜索随机树算法规划出初步全局优化路径,局部避碰规划是在全局优化路径的同时,通过基于滚动窗口的环境探测和碰撞规则,对动态障碍物实施有效的局部避碰策略,从而使机器人安全顺利地到达目的地。仿真实验结果说明该方法具有可行性。     

Scaling problems in dynamic system simulation

Dynamic system simulation using analog or hybrid computers requires scaling of the variables. This problem arises also in digital simulation when fixed-point arithmetic is used. Prediction of variables extrema is the most difficult problem in scaling. Authors developed methods of estimation of variables based on direct Lyapunov method. General solution for the linear time-invariant systems was obtained. Extension of this solution for some classes of non-linear systems common in control engineering is also shown.