A temporal blackboard for a multi-agent environment

The multi-agent system paradigm emerges as an interesting approach in the Knowledge Based System (KBS) field, when distributed problem-solving techniques are required for solving problems that can be represented as a collection of groups of cooperating intelligent individuals. A key concept in the multi-agent systems is the interaction between agents. On the other hand time plays a crucial role in a wide range of KBS applications. Temporal reasoning and representations consists of formalizing the notion of time and providing means to represent and reason about the temporal aspects of knowledge. This paper presents a framework for agent communication based on the blackboard paradigm which is able to manage temporal information, and it provides its multiple access and coherence management protocols.     

Embedable problem-solving architectures: a study of integratingOPS5 with UMass GBB

The requirements of a problem-solving architecture that can be tightly embedded within other architectures and coexist with multiple instances of itself and of other problem-solvers are discussed. The additional effort needed to produce an embedable problem-solving architecture is minor compared to the substantial increase in applicability of the architecture. A specific need for embedable problem-solvers arose with the University of Massachusetts Generic Blackboard Framework (UMass GBB). UMass GBB is based on the blackboard paradigm, which naturally integrates heterogeneous problem-solving representations as individual knowledge sources (KSs). This need is met by developing general specifications for embedable problem-solving architectures, and the specifications are used to modify the public-domain version of OPS5 in order to embed it as an integral KS language within UMass GBB. The OPS5 modifications result in an easily integrated GBB KS language (distributed with the UMass GBB system) that has been used in several GBB applications     

ACHIEVING FLEXIBLE AUTONOMY IN MULTIAGENT SYSTEMS USING CONSTRAINTS

Organizations influence many aspects of our lives. They exist for one reason: they can accomplish things that individuals cannot. While recent work in high-autonomy systems has shown that autonomy is a critical issue in artificial intelligence (AI) systems, these systems must also be able to cooperate with and rely on one another to deal with complex problems. The autonomy of such systems must be flexible, in order that agents may solve problems on their own as well as in groups. We have developed a model of distributed problem solving in which coordination of problem-solving agents is viewed as a multiagent constraint-satisfaction planning problem. This paper describes the experimental testbed that we are currently developing to facilitate the investigation of various constraint-based strategies for addressing the coordination issues inherent in cooperative distributed problem-solving domains.     

Communication infrastructure in distributed scheduling

The emergence of distributed artificial intelligent (DAI) introduced a new approach to solve scheduling problems by a set of scheduling systems that interact with each other in the problem-solving process. In this paper, we describe a communication infrastructure to handle connection and communication between distributed Internet scheduling systems for distributed applications. First, we present an agent model of distributed scheduling systems where agents can communicate and coordinate activities with each other via an agent communication language. Then, we define the syntax and semantics for the agent communication languages, and negotiation mechanism. Following that, we discuss the design and development of the prototype for the multi-agent scheduling systems. We conclude with a discussion of communication issues for heterogeneous agent-based scheduling systems to solve distributed scheduling problems.     

A computational model for distributed knowledge systems with learning mechanisms

This paper addresses the issues of machine learning in distributed knowledge systems, which will consist of distributed software agents with problem solving, communication and learning functions. To develop such systems, we must analyze the roles of problem-solving and communication capabilities among knowledge systems. To facilitate the analyses, we propose a computational model: LPC. The model consists of a set of agents with (a) a knowledge base for learned concepts, (b) a knowledge base for problem solving, (c) prolog-based inference mechanisms and (d) a set of beliefs on the reliability of the other agents. Each agent can improve its own problem-solving capabilities by deductive learning from the given problems, by memory-based learning from communications between the agents and by reinforcement learning from the reliability of communications between the other agents. An experimental system of the model has been implemented in Prolog language on a Window-based personal computer. Intensive experiments have been carried out to examine the feasibility of the machine learning mechanisms of agents for problem-solving and communication capabilities. The experimental results have shown that the multiagent system improves the performance of the whole system in problem solving, when each agent has a higher learning ability or when an agent with a very high ability for problem solving joins the organization to cooperate with the other agents in problem solving. These results suggest that the proposed model is useful in analyzing the learning mechanisms applicable to distributed knowledge systems.     

分布式黑板模型的设计与实现

由于传统的黑板模型是基于串行硬件环境之上的集中试验问题求解模型，因此其效率和可靠性受到很大限制，使以此模型为结构的很多专家系统无法实用化。为了解决这个问题，我们基于Ｎｏｖｅｌｌ网络环境，研制了一种分布式黑板模型，该模型具有可靠性高，求解速度快的特点。本文介绍了这个模型的体系结构，描述了系统采用的一些技术。     

Scale-guided object matching for case-based reasoning

Case-Based Reasoning (CBR) can be seen as a problem-solving paradigm that advocates the use of previous experiences to limit search spaces and to reduce opportunities for error repetition. In this paradigm, the case at hand is compared against former experiences to select from a set of possible courses of action the best one. A comparison method is required to ensure that the most resembling experience is, in fact, chosen to drive the problem-solving process. This paper discusses an object-oriented framework that provides a scale-guided measure of similarity between objects, and shows how this framework can be applied for case-based reasoning, drawing examples from device diagnosis.     

On making expert systems more like experts

Abstract: Expert systems still lack the skill of an expert when it comes to providing explanations of the results of expert reasoning. This is because while such systems may implement knowledge which is sufficient to mimic the performance of an expert, they do not necessarily model the expertise upon which that performance is based. Such a model must include knowledge of that domain's terminology, knowledge of domain facts, and knowledge of problem-solving methods. The Explainable Expert Systems project has been exploring a new paradigm for expert system development that is intended to capture such missing knowledge and make it available for explanation. This paper will discuss the principles behind this paradigm and consider two systems that employ it.     

Stepwise refinement and problem solving

The possibility of reexpressing the traditional notion of stepwise refinement as a combination of general problem-solving activities that are based on paradigms taken from artificial intelligence research is discussed. This reexpression can form the basis for a more explicit view of programming as a problem-solving activity. Experiments in which each step of the refinement process is encoded into problem solving activities are described. 26 examples of code implementation using the stepwise refinement of pseudocode have been analyzed. The presence of certain combinations of activities suggest that programmers are implicitly emulating certain paradigms that have proved useful in solving complex problems. Also, a particular paradigm and its associated activities seem to be applied often throughout the refinement sequence for a given problem. The nature of the problem to be solved influences the type of activities performed to achieve a solution, as well as the problem-solving paradigm that they implicitly support     

Towards a temporal coherence management in real-time knowledge-based systems

The multi-agent system paradigm emerges as an interesting approach in the Knowledge-Based System (KBS) field when distributed problem-solving techniques are required. On the other hand, temporal representation and reasoning problems arise in a wide range of KBS application areas where time plays a crucial role. In this paper, we show that when agents run concurrently and access common temporal data, some problems of coherence arise. We analyse the different cases in which an incoherence in temporal information can occur and provide a method to tackle this problem. In this method, conflict management is handled by means of exception handlers and control rules allowing the users to explicitly define their own strategy for temporal coherence solving.     

Socially distributed cognition in loosely coupled systems

Distributed cognition provides a theoretical framework for the analysis of data from socio-technical systems within a problem-solving framework. While the approach has been applied in tightly constrained activity domains, composed of well-structured problems and highly organised infrastructures, little is known about its use in other forms of activity systems. In this paper, we explore how distributed cognition could be applied in less well-constrained settings, with ill-structured problems and loosely organised resource sets, critically reflecting on this using data from a field study. The findings suggest that the use of distributed cognition in an augmented form can be useful in the analysis of a wide range of activity systems in loosely coupled settings.     

Mobile-Process-Based Parallel Simulation

Our focus is on the novel use of a process-oriented methodology in software systems for parallel simulation on distributed memory. To the best of our knowledge, the few existing systems which adopt a process view strictly use message passing to effect process interaction in distributed-memory settings. As a result, these systems avoid scenarios in which processes directly access passive but shared components. This can restrict the manner in which a system is modeled and hinder the phase of distributed model construction. In this paper, we propose an approach which utilizes mobile processes in distributed-memory simulation systems. The approach entails the migration of a requesting process with its timestamp to the remote site hosting the requested passive object. Major advantages of this approach include one-time transmission, fixed communication topology, and increased locality of reference. Empirical results based on lightweight processes show that the mobile process paradigm can be as efficient as the message-passing paradigm.     

Trends in cooperative distributed problem solving

The authors present an overview of cooperative distributed problem solving (CDPS), an emerging research area that combines aspects of AI (artificial intelligence) and distributed processing. CDPS can be used to study how a loosely coupled network of sophisticated problem-solving nodes can solve a complex problem which consists of a set of interdependent subproblems. Subproblems arise because of spatial, temporal, and functional distribution of data, knowledge, and processing capabilities. Application areas include distributed interpretation, distributed planning and control, cooperating expert systems, and computer-supported human cooperation. The authors survey the important approaches and empirical investigations that have been developed. The approaches covered include negotiation, functionally accurate cooperation, organizational structuring, multiagent planning, sophisticated local control, and theoretical frameworks     

Learning to Improve Coordinated Actions in Cooperative Distributed Problem-Solving Environments

Coordination is an essential technique in cooperative, distributed multiagent systems. However, sophisticated coordination strategies are not always cost-effective in all problem-solving situations. This paper presents a learning method to identify what information will improve coordination in specific problem-solving situations. Learning is accomplished by recording and analyzing traces of inferences after problem solving. The analysis identifies situations where inappropriate coordination strategies caused redundant activities, or the lack of timely execution of important activities, thus degrading system performance. To remedy this problem, situation-specific control rules are created which acquire additional nonlocal information about activities in the agent networks and then select another plan or another scheduling strategy. Examples from a real distributed problem-solving application involving diagnosis of a local area network are described.     

Collaboration rules for autonomous software agents

Can autonomous software agents that are distributed over a computer network collaborate effectively? Both empirical evidence and theory suggest that they can. Moreover, there seem to be simple rules for designing problem-solving organizations in which collaboration among such agents is automatic and scale-effective (adding agents tends to improve solution-quality; adding computers tends to improve solution-speed). This paper develops some of these rules for off-line problems and argues that they can be extended for the on-line (real-time) control of power systems.     

Framework for modeling and analysis of distributed problem-solving systems

Petri nets have the basic concepts necessary to model distributed systems with asynchronous processes. Petri nets are not directly applicable to certain kinds of systems like distributed intelligent systems (DISs). These are complex systems where multiple intelligent agents cooperate through communication to achieve the solution to a problem. The paper identifies the limitations of ordinary Petri nets for modeling DISs and proposes extensions. The extended Petri net incorporates colored tokens, inhibition arcs, non-primitive places and transitions, multiple copies of tokens and cumulative places. It is called a distributed problem-solving Petri net. The definitions and analysis techniques are given and illustrated by means of an example.     

Using constraint propagation in blackboard systems: a flexiblesoftware architecture for reactive and distributed systems

Constraint propagation is explained, and the integration of problem-solving components is discussed. Validity and completeness results are considered, and the extension of constraint propagation to reactive and distributed systems using a blackboard architecture is addressed     

Collecting Metrics for CORBA-Based Distributed Systems

The Common Object Request Broker Architecture (CORBA) supports the creation of distributed systems that cross processor, language and paradigm boundaries. These systems can be large and complex entities that consume considerable resources in their creation and execution. Measurements of characteristics of software systems is an important area of study in general and of particular interest for distributed systems. In this paper, we present a specific technique for instrumenting components in a distributed system. The technique constructs a wrapper around the component being measured. The wrapper monitors interactions with the ORB (Object Request Broker) and other components. Each wrapper mimics the interface of the component that it is wrapping so that the remaining objects in the system do not need modification. Two approaches to wrapping the component are presented and contrasted. The result is an efficient and modular technique that can quickly be applied to a component.