Sung-Do Chi

Symbolic discrete event system specification

Extending discrete event modelling formalisms to facilitate greater symbol manipulation capabilities is important to further their use in intelligent control and design of high autonomy systems. This paper defines an extension to the discrete event system specification (DEVS) formalism that facilitates symbolic expression of event times by extending the time base from the real numbers to the field of linear polynomials over the reals. A simulation algorithm is developed to generate the branching trajectories resulting from the underlying nondeterminism. The extended formalism offers a convenient means to conduct multiple, simultaneous explorations of model behaviors. Examples of application are given with concentration on fault model analysis.<<ETX>>

Model-based task planning system for a space laboratory environment

This paper describes the design of a model-based autonomous planning system that will enable robots to manage a space-borne chemical laboratory. In a model-based planning system, knowledge is encapsulated in the Ibrm of models at the various layers to support the predefined system objectives. Thus the model-based approach can he considered as an extended planning paradigm which is able to base its planning, control, diagnosis, repair, and other activities on a variety of objectives-related models. We employ a System Entity Structure/Model Base framework to support autonoiious system design through the ability to generate a family of planning alternatives as well as to build hierarchical event-based control structures. The model base is a multi-level, multi-abstraction, and multiformalism system organized through the use ni system morphisms to integrate related models.

Model-based architecture for high autonomy systems

This paper presents the principles for design of autonomous systems whose behavior is based on models that support the various tasks that must be performed. We propose a model-based architecture aimed at reducing the computational demands required to integrate high level symbolic models with low level dynamic models. Model construction methods are illustrated to outfit such an architecture with the models needed to meet assigned objectives.

Using the CESM Shell to Classify Wafer Defects from Visual Data

Automatic visual wafer inspection will increase productivity and improve product quality of integrated circuit (IC) chips on wafers. Recently, research on such inspection has been focused on problems for classification of defects on wafers to rectify wafer fabrication errors by comparing them with predefined specification standards. This paper describes the development of a knowledge-based system which classifies defects in the recti-linear type wafer images for VLSI chips. A consultation system shell called CESM (Classification Expert System Maker) has been used to develop the classification system. CESM supports integration of low level processing, feature extraction, and final decision stages.

DEVS-BASED INTELLIGENT CONTROL: SPACE-ADAPTED MIXING SYSTEM EXAMPLE

This paper reviews a methodology for event-based intelligent control employing the DEVS (discrete event system specification) formalism. In this control paradigm, the controller expects to receive confirming sensor responses to its control commands within definite time windows determined by its DEVS model of the system under control. We apply the DEVS-based intelligent control paradigm to a space-adapted mixing system. The event-based approach is compared with conventional sequential control methods.

Discrete event modeling and simulation for traffic flow analysis

This paper presents a discrete event modeling and simulation framework for analyzing the traffic flow. The proposed framework can provide a convenient means for evaluating signal control strategies at the operation level of advanced traffic management systems and for generating the simulation-based forecasting information for advanced traveler information systems. To do this, the authors have employed the system entity structure/model base (SES/MB) framework which integrates the dynamic-based formalism of simulation with the symbolic formalism of AI. Several simulation tests demonstrate the techniques.

Hierarchical Encapsulation and Abstraction Principle (heap) for Autonomous System Development

A general approach t o task-based model development is summarized in a Hierarchical Encapsulation and Abstraction Principle (HEAP) and this principle is briefly illustrated in the planning, operations and diagnosis task domains. 1 Brief Overview on Model-Base Autonomous System Architecture To cope with complex objectives, an autonomous system requires integration of symbolic and numeric data, qualitative and quantitative information, reasoning and computation. A pure AI approach is too qualitatively oriented to handle quantitative information very well. For example, classic AI planning approaches [4, 5 , 61 do not consider the timing effects, which should be of primary concern in representing our dynamic world. On the other hand, control researchers have a fairly narrow view-point, so that they mainly focus on refinement rather than robustness of a system [7], and they usually consider only the normal operational aspects of a system. However, autonomous systems have to deal with abnormal behavior of a system as well. Thus, it is crucial to have a strong formalism and an environment that allows coherent integration of symbolic and numeric informations in a valid representation process to deal with a complex dynamic world. Approaches to design various autonomous component models for planning, operation, and diagnosis have previously been developed in their respective research fields so that there are many overlaps as well as inconsistencies in assumptions. In an integrated system, such components cannot be considered independently. For example, planning requires execution, and diagnosis is activated when anomalies are detected during execution. The model-based autonomous system architecture features a model base a t the center of its planning, operation, diagnosis, and fault recovery strategies [2]. In this way, it integrates AI symbolic models and controltheoretic dynamic models into a coherent system. Endomorphism refers to the existence of a homomorphism from an object to a sub-object within it, the part (sub-object) then being a model of the whole [8]. In order to control an object, a high autonomy system needs a corresponding model of the object to determine the particular action to take. The internal model used by the system and its world base model are related by abstraction, i.e., some form of homomorphic (i.e., endomorphic relation. The inference mation for interacting with the real world object. By “world base model” we mean the most comprehensive model of the world available to the system whether it exists as a single object or as a family of partial models in the model base. Typical expert systems comprise a domainindependent inference engine and a domain-dependent knowledge base. The inference engine examines the knowledge base and decides the order in which inferences are made. The engine-based modelling approach provides a clear separation between the domain -dependent model base and the domain-independent inference engine. It facilitates the automatic generation of a model base using endomorphisms. Figure l shows the engine-based modelling concept and examples of autonomous system components realized using the concept. engine asks its internal mo d el for the necessary infor-

Endomorphic modeling of intelligent system: intelligent card game player

This paper presents an endomorphic modeling methodology for designing intelligent systems. An intelligent system can determine by itself using its knowledge of the world and adapt itself to continuously changing circumstances. We have developed an intelligent endomorphic system by integrating the decision making component and knowledge based internal model with the internal model construction model. Learning capabilities are established using the inductive reasoning and adaptive expert system techniques. The proposed methodology has been successfully applied to the design of an intelligent card game player capable of supporting the intelligent learning and decision making.

Numerical Properties of Trajectory Representations of Polynomial Matrices

Abstract In the early 1970s, several researchers reported results relating to the design of multivariable linear systems represented by polynomial matrices. In particular, the book by Wolovich (1974) found widespread resonance. In the sequel, however, the success of these techniques was rather Emited since a manual application of the proposed algorithms is atrocious for all but the most trivial systems, whereas appropriate CACSD tools that would make use of these techniques were not available. The main reasons for this deficiency were twofold: (i) polynomial matrix operations require symbolic processing, a computational technique that was still in its infancy in the 1970s, and (ii) the numerical properties of frequency domain operations were considered dubious. In this paper, the numerical properties of frequency domain operations are analyzed. The two classical data representations (coefficients and roots) are reviewed, and two new data representations, trajectories and coefficient spectra, are proposed.