Distributed Problem Solving Research Articles

Supporting Online PBL: Design Considerations for Supporting Distributed Problem Solving

This study considers how four groups of education graduate students worked in a distributed problem-based learning (PBL) environment using an asynchronous threaded discussion tool. The analyses consider how the students were able to interact with each other using the tool and how they worked together to define the problem and identify key issues to explore. The outcomes include some design considerations for tools to support these activities as well as insight about how students may work together to solve a problem in a distributed environment.

A multi-agent decision support system for stock trading

A distributed problem solving system can be characterized as a group of individual cooperating agents running to solve common problems. As dynamic application domains continue to grow in scale and complexity, it becomes more difficult to control the purposeful behavior of agents, especially when unexpected events may occur. This article presents an information and knowledge exchange framework to support distributed problem solving. From the application viewpoint the article concentrates on the stock trading domain; however, many presented solutions can be extended to other dynamic domains. It addresses two important issues: how individual agents should be interconnected so that their resources are efficiently used and their goals accomplished effectively; and how information and knowledge transfer should take place among the agents to allow them to respond successfully to user requests and unexpected external situations. The article introduces an architecture, the MASST system architecture, which supports dynamic information and knowledge exchange among the cooperating agents. The architecture uses a dynamic blackboard as an interagent communication paradigm to facilitate factual data, business rule, and command exchange between cooperating MASST agents. The critical components of the MASST architecture have been implemented and tested in the stock trading domain, and have proven to be a viable solution for distributed problem solving based on cooperating agents.

Agent Strategy and Resource Allocation Dynamism on Market-Oriented Programming-Basic Analysis

Market-oriented programming is a multi-agent-based concept to facilitate distributed problem solving. In market-oriented programming we take the metaphor of an economy computing multiagent behaviour literally, and directly implement the distributed computation as a market price system. In a market-oriented programming approach to distributed problem solving, the resource allocation for a set of computational agents is derived by computing competitive market of an artificial economy. Since heterogeneous agent strategies have intensive effects on the resource allocation, dynamism, we analyse and systematise their relationship in supply chain simulation with a virtual market.The behaviour of the system can be analysed in agent interactions based on economic strategies.

Collaborative Metacomputing with IceT

The IceT metacomputing system is based on the principles of collaborative resource sharing, lightweight software frameworks, and efficient execution in heterogeneous environments. In this project, the traditional model for distributed computing environments has been extended to facilitate the controlled and secure exchange of hardware and software resources, and to support programming paradigms that achieve a balance between portability and efficiency. The current IceT system is Java-based, and presents a framework within which non-persistent collections of resources, users, and processes may interact to accomplish distributed problem solving. This paper describes the architecture and design principles of IceT, its operational model and support for the execution of distributed applications, and orthogonal subsystems that were developed to support the concept of cooperative metacomputing.

Scalability and information agents

Having fast and dependable access to the most relevant information available is of the utmost importance in a competitive information-oriented society. Ensuring transparent and dependable access to a large number of heterogeneous, ill-structured and often distributed data and information sources is a complex problem with many different facets. Over time a large variety of very different approaches have been developed. Among the many competing approaches, information agents seem to be particularly well suited to the challenges of the information cyberspace due to their highly adaptive and distributed problem solving. Only information agents seem capable to offer the much needed user centric access to the myriads of data and information sources accessible via the web. But just like other agents, they require significant computational resources making it difficult to build scalable systems.This paper has two aims. First it is an attempt to draw attention to the scalability challenges in developing systems consisting of large numbers of information agents. Second, it presents a CORBA based framework called DICE for building information agents and reports about its use in developing real world systems based on information agents.

Mobile agents applications

Automatic, autonomous browsing has an increasingly important task in information discovery and assisted browsing on the Internet. Where users could once keep up to date with information of interest on the Internet, the recursive growth of the network has made this process increasingly time consuming and less rewarding. Presents two possible solutions to this problem: Data Agents and CollaborAgents which were developed with IBM’s Aglet Workbench – a particular implementation of mobile agents. Also surveys the agent technology and discusses the agent building package used to develop both mentioned applications. Concludes that the future of local interaction, reduced network loading, server flexibility and application autonomy which are supported by mobile agent technology, all help to provide a level agility above distributed problem solving.

Comparison of constraint logic programming and distributed problem solving: a case study for interactive, efficient and practicable job-shop scheduling

The job-shop scheduling issue is more and more described not only in terms of efficiency (e.g. Makespan), but also in terms of interactivity and practicability. The aim of this paper is to evaluate the ability of two approaches inherited from artificial intelligence domain to contribute to the solving of this issue: Constraint Logic Programming (CLP) on the one hand, and, on the other hand, Distributed Problem Solving (DPS) inherited from Distributed Artificial Intelligence (DAI). This analysis is achieved through the use of two specific computerised tools: Constraint Handling In Prolog (CHIP) as a CLP software and the Distributed Production Scheduling System (DPSS) as a distributed problem solving system. These tools are then evaluated in terms of interactivity, efficiency and practicability. Interactivity is discussed according to qualitative points of view such as the ability to provide efficient decision support, a set of alternative solutions and the possibility to parameterise the algorithms. Efficiency is described in terms of optimality or sub-optimality by the analysis of the Makespan criterion vs. fixed computation time. Practicability is associated to the industrial viability of the methods: ability to cope with real industrial case study or ability to face real industrial contexts. Evaluation is then performed through a multiple criteria analysis. This analysis is achieved given an increasing number of operations to perform. The results highlight the high complementary level of these two approaches, allowing us to provide a framework for a joint integration, which shall be optimised when taking into account the assets of each approach according to the three evaluation criteria.

107 仮想市場型サプライチェーンにおけるエージェントの戦略と資源配分特性(OS-5 生産システム)

Market-oriented programming is a multi-agent-based concept to facilitate distributed problem solving. In market-oriented programming we take the metaphor of an economy computing multi-agent behaviour literally, and directly implement the distributed computation as a market price system. In a market-oriented programming approach to distributed problem solving, the resource allocation for a set of computational agents is derived by computing competitive market of an artificial economy. Since heterogeneous agent strategies have intensive effects on the resource allocation dynamism, we analyse and systematise their relationship in supply chain simulation with virtual market.

Distributed problem solving in social insects

In a social insect colony, large numbers of individuals all follow the same set of behavioral rules. Without centralized control, these individuals' interactions with each other and with their environment result in the allocation of individuals to various tasks, and in the distribution of foragers among available food sources. We review this highly parallel and distributed form of information processing, discussing its potential sophistication, its actual performance in various groups of social insects, its general strengths and liabilities, and finally, the adaptations that compensate for these liabilities.

Distributed Agents for Problem Solving

Agent-based computing represents a new and very significant paradigm for building a wide range of software applications. In this paper, we describe our research project on the development of a software agent-based architecture for Distributed Problem Solving (DPS). Such agents must be heterogeneous and adaptive in nature to satisfy increasing requests due to cooperation capabilities exhibited by DPS applications (e.g., computer vision applications such as biomedical imaging and image retrieval on the Web, distributed and intelligent information retrieval, and so on). We give a brief introduction to the overall architecture design and focus our attention on the lowest level agents, called W-bots (Working Software Robots). We define the nature of such agents and describe their ways of interaction and their adaptive capabilities.

Multiagent Systems: A Survey from a Machine Learning Perspective

Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a domain. Traditionally, DAI has been divided into two sub-disciplines: Distributed Problem Solving (DPS) focuses on the information management aspects of systems with several components working together towards a common goals Multiagent Systems (MAS) deals with behavior management in collections of several independent entities, or agents. This survey of MAS is intended to serve as an introduction to the field and as an organizational framework. A series of general multiagent scenarios are presented. For each scenario, the issues that arise are described along with a sampling of the techniques that exist to deal with them. The presented techniques are not exhaustive, but they highlight how multiagent systems can be and have been used to build complex systems. When options exist, the techniques presented are biased towards machine learning approaches. Additional opportunities for applying machine learning to MAS are highlighted and robotic soccer is presented as an appropriate test bed for MAS. This survey does not focus exclusively on robotic systems. However, we believe that much of the prior research in non-robotic MAS is relevant to robotic MAS, and we explicitly discuss several robotic MAS, including all of those presented in this issue.

Distributed artificial intelligence for FMS scheduling, control and design support

The aim of this paper is to point out some of the abilities of Distributed Artificial Intelligence in the domain of scheduling, control and design support of Flexible Manufacturing Systems. A distributed management system is proposed, based on Distributed Problem Solving, sub-field of Distributed Artificial Intelligence. The basic concepts are the concept of Resource Management Entity to ensure local optimization of the management of resources and the concept of cooperation to provide ability for global and local consistency. The management of resources is associated to activities such as scheduling, control or simulation. It is shown that this system computes not only practicable schedulings, but also presents, on the one hand, some abilities in supporting the design and the robust optimization of Flexible Manufacturing Systems, and, on the other hand, some abilities in supporting real-time control of such systems. This enables, in future works, to design a Distributed Decision Support System for integrated scheduling, control and design support of production systems.

An Empirical Approach to a Theory of Coordination. Part I: Design Principles and First Results

Coordination is understood to consist of a set of mechanisms necessary for the effective operation of Intelligent Agent Societies (IASs). In building such societies, it is important to design and implement coordination in accordance with the known requirements and anticipated working conditions of the IAS in question. Currently, there is little theoretical support that could help in this process. We outline the approach and design principles of our work on automatically generating an empirically-based theory of coordination. We also describe the first set of results obtained, which prove the feasibility of the approach. We are concerned with Distributed Problem Solving (DPS) systems in which all agents share an identical goal structure and fully collaborate, as opposed to Multi-Agent Systems (MAS) in which agents may also compete with one another. Our investigation is based on an easily modifiable and parametrizable generic IAS, the P-system, a metaphorical and abstract version of our earlier work, the Distributed Control of Nationwide Manufacturing Operations. The P-system shares characteristic properties with most, if not all, IASs. It was used for a sequence of rigorously designed experiments in which control variables operated under well-defined conditions and performance measures were observed. We infer, from the statistical analysis of these data, characteristic and important descriptors of the organization and functioning of IASs in general. The resulting relations should produce insight into the fundamental issues of coordination, provide design tools and guidelines for the construction of new IASs, and lend support in trouble-shooting existing ones.

A Study on Dynamic Market-Oriented Programming

Market price systems constitute a well-understood class of mechanisms that provide effective decentralisation of decision making with minimal communication overhead. In a market-oriented programming approach to distributed problem solving, the resource allocations for a set of computational agents are derived by computing competitive equilibrium of an artificial economy in static conditions. In this paper we newly propose a dynamic market-oriented programming. Each agent has transitional dynamic utilities and tries to find equilibrium point by evaluating the tradeoffs of acquiring different resources in dynamic environment. Careful constructions of the dynamic decision process according to economic principles can lead to efficient distributed resource allocation, and the behaviour of the system can be analysed in economic terms.

Emergent cooperative goal-satisfaction in large-scale automated-agent systems

Cooperation among autonomous agents has been discussed in the DAI community for several years. Papers about cooperation (Conte et al., 1991; Rosenschein, 1986), negotiation (Kraus and Wilkenfeld, 1991), distributed planning (Conry et al., 1988), and coalition formation (Ketchpel, 1994; Sandholm and Lesser, 1997), have provided a variety of approaches and several algorithms and solutions to situations wherein cooperation is possible. However, the case of cooperation in large-scale multi-agent systems (MAS) has not been thoroughly examined. Therefore, in this paper we present a framework for cooperative goal-satisfaction in large-scale environments focusing on a low-complexity physics-oriented approach. The multi-agent systems with which we deal are modeled by a physics-oriented model. According to the model, MAS inherit physical properties, and therefore the evolution of the computational systems is similar to the evolution of physical systems. To enable implementation of the model, we provide a detailed algorithm to be used by a single agent within the system. The model and the algorithm are appropriate for large-scale, dynamic, Distributed Problem Solver systems, in which agents try to increase the benefits of the whole system. The complexity is very low, and in some specific cases it is proved to be optimal. The analysis and assessment of the algorithm are performed via the well-known behavior and properties of the modeling physical system.

A distributed architecture and negotiation protocol for scheduling in manufacturing systems

This paper deals with a new architecture and negotiation protocol for the dynamic scheduling of Manufacturing Systems. The architecture is based on two paradigms: Multi-Agent Systems and Holonic Systems. The main contribution in the architecture is the development of Holons representing tasks and resources. The well-known Contract Net Protocol [R. Davis, R. Smith, Negotiation as a metaphor for distributed problem solving, Artificial Intelligence, 20, (1), (1983) 63–109] has been adapted to handle temporal constraints and to deal with scheduling conflicts. The purpose of this protocol is to dynamically assign operations to the resources of the Manufacturing System to accomplish the proposed tasks. This protocol involves a renegotiation phase whenever exceptions appear. It also deals with conflict situations, namely, with the case of the `indecision problem'. The used approach assumes that deadlines are the most important constraints to consider. Thus the acceptance or refusal of a resource for a specific operation depends on the capability of executing the operation within the specified deadline.

Using the Concept of Intelligent Agents in FaultManagement of Distributed Services

This paper proposes the application of concepts from the area of intelligent agents to overcome deficiencies of existing management architectures regarding distribution of functionality and flexibility. Its main contribution is the proposal of a methodology for a flexible, distributed realization of complex management tasks. The main application areas are distributed services which are complex pieces of software, distributed across various heterogeneous end systems in a network. Mostly, they rely on the provision of other services as well. The approach relies on well-known concepts, such as cooperative distributed problem solving and intelligent agents, and offers a framework to combine these two concepts, providing a step on the roadmap to a flexible, distributed management architecture. The assessment of the approach is displayed throughout the paper by scenarios from the area of nontime critical fault management.

A measure of information reuse to compare distributed protocols

In the framework of distributed problem solving we introduce two communication protocols, the request-subrequest protocol and the local-caching protocol, which are based on the hypothesis of minimal and maximal reuse of available information, respectively. The optimal solution of a distributed problem can be reached according to different protocols. The notion of measure of reuse related to each protocol has been formally introduced to provide a criterion to compare and to choose the right protocol.

ViSe2 - An Agent-Based Expert Consulting System with Efficient Cooperation

ViSe2 l is an expert consulting system which employs software agents to manage distributed knowledge sources. These individual software agents solve users’ problems either by themselves or via cooperation. The efficiency of cooperation plays a serious role in Distributed Problem Solving (DPS) and Multi-Agent Systems (MAS). We have focused on the development of a twin-base approach for agents to model the capabilities of each other, and thus achieve efficient cooperation. The current version of the ViSe2 implementation is an experimental model of an agent-based expert system. Compared with other cooperation approaches in Distributed Artificial Intelligence (DAI) area, the results received so far indicate that the ViSe2 agents serve their users in an efficient cooperation manner.

Constraint-based protocols for distributed problem solving

Distributed Problem Solving (DPS) approaches decompose problems into subproblems to be solved by interacting, cooperative software agents. Thus, DPS is suitable for solving problems characterized by many interdependencies among subproblems in the context of parallel and distributed architectures. Concurrent Constraint Programming (CCP) provides a powerful execution framework for DPS where constraints define local problem solving and the exchange of information among agents declaratively. To optimize DPS, the protocol for constraint communication must be tuned to the specific kind of DPS problem and the characteristics of the underlying system architecture. In this paper, we provide a formal framework for modeling different problems and we show how the framework applies to simple yet generalizable examples.