Utilizing semantic networks to database and retrieve generalized stochastic colored Petri nets

Previous work has introduced the Planning Coordinator (PCOORD), a coordinator functioning within the hierarchy of the Intelligent Machine Mode. Within the structure of the Planning Coordinator resides the Primitive Structure Database (PSDB) functioning to provide the primitive structures utilized by the Planning Coordinator in the establishing of error recovery or on-line path plans. This report further explores the Primitive Structure Database and establishes the potential of utilizing semantic networks as a means of efficiently storing and retrieving the Generalized Stochastic Colored Petri Nets from which the error recovery plans are derived

A Conceptual Framework for Adapation

We present a white-box conceptual framework for adaptation. We called it CODA, for COntrol Data Adaptation, since it is based on the notion of control data. CODA promotes a neat separation between application and adaptation logic through a clear identification of the set of data that is relevant for the latter. The framework provides an original perspective from which we survey a representative set of approaches to adaptation ranging from programming languages and paradigms, to computational models and architectural solutions

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

A brief network analysis of Artificial Intelligence publication

In this paper, we present an illustration to the history of Artificial Intelligence(AI) with a statistical analysis of publish since 1940. We collected and mined through the IEEE publish data base to analysis the geological and chronological variance of the activeness of research in AI. The connections between different institutes are showed. The result shows that the leading community of AI research are mainly in the USA, China, the Europe and Japan. The key institutes, authors and the research hotspots are revealed. It is found that the research institutes in the fields like Data Mining, Computer Vision, Pattern Recognition and some other fields of Machine Learning are quite consistent, implying a strong interaction between the community of each field. It is also showed that the research of Electronic Engineering and Industrial or Commercial applications are very active in California. Japan is also publishing a lot of papers in robotics. Due to the limitation of data source, the result might be overly influenced by the number of published articles, which is to our best improved by applying network keynode analysis on the research community instead of merely count the number of publish.Comment: 18 pages, 7 figure

On Object Oriented Nondeterministic Supervisory Control

Implementation of complex discrete event fabrication processes can be considerably simplified by use of general reusable software modules representing the physical components. At the same time, construction of the control system can be facilitated by applying the supervisory control theory for the automatic generation of control laws. These two aspects can be joined into a general concept with object-oriented modeling and control law synthesis as foundations. The goal is to allow an operator to specify operation lists describing the required sequences of operations for the manufacturing of the product, independently of constraints given by a specific plant. With a suitable model of the capabilities and constraints of the resources of that plant, a product route can be automatically generated from the operation list. Such a product route describes all available paths through the system, for each type of product, irrespective of any other type of product that may be simultaneously present within the production system. Given a set of product routes and a model of the plant, control laws guaranteeing production according to those product specifications can be synthesized. Based on the supervisory control theory, using interleaved product routes as specification, we show how such control laws can be synthesized. An added complexity is that the specification becomes non-deterministic, in the sense that the same string of events can lead to different system states. We show that the supervisory control theory can be used with non-deterministic specifications assuming certain properties. An algorithm for synthesis of a non-deterministic supervisor is presented. We also describe an object-oriented modeling approach to discrete event fabrication processes. It is shown that the properties that have been defined as necessary for the non-deterministic supervisory approach are immediate by the modeling approach. Thus, we show that the approach to non-deterministic supervisory control can be combined with object-oriented modeling techniques, and so we have a powerful framework for implementing control of large and complex discrete event fabrication processes

Second Workshop on Modelling of Objects, Components and Agents

This report contains the proceedings of the workshop Modelling of Objects, Components, and Agents (MOCA'02), August 26-27, 2002.The workshop is organized by the 'Coloured Petri Net' Group at the University of Aarhus, Denmark and the 'Theoretical Foundations of Computer Science' Group at the University of Hamburg, Germany. The homepage of the workshop is: http://www.daimi.au.dk/CPnets/workshop02

Modelling and analyzing adaptive self-assembling strategies with Maude

Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA

On Agent Communication in Large Groups

The problem is fundamental and natural, yet deep - to simulate the simplest possible form of communication that can occur within a large multi-agent system. It would be prohibitive to try and survey all of the research on communication in general so we must restrict our focus. We will devote our efforts to synthetic communication occurring within large groups. In particular, we would like to discover a model for communication that will serve as an abstract model, a prototype, for simulating communication within large groups of biological organisms