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AM/AG MODEL: A HIERARCHICAL SOCIAL SYSTEM METAPHOR FOR distributed problem solving

International Journal of Pattern Recognition and Artificial Intelligence 1990年第3期4卷 473-487页

作者： D.G. SHIN J. LEONE Computer Science and Engineering Department University of Connecticut Storrs CT 06269–3155 USA

This work explores a distributed problem solving (DPS) approach, namely the AM/AG (Amplification/Aggregation) model. The AM/AG model is a hierarchic social system metaphor for DPS based on Mintzberg’s model of organizations. At the core of the model are information flow mechanisms, namely, amplification and aggregation . Amplification is a process of decomposing a given task, called an agenda , into a set of subtasks with magnified degree of specificity and distributing them to multiple processing units downward in the hierarchy. Aggregation is a process of combining the results reported from multiple processing units into a unified view, called a resolution , and promoting the conclusion upward in the hierarchy. Amplification is discussed in detail. A set of generative rules is introduced. Each rule specifies a set of actions for transforming an input agenda into other forms with higher specificity. The proposed model can be used to account for the memory recall process which makes associations between vast amounts of related concepts, sorts out the combined results, and promotes the most plausible ones. An example of memory recall is used to illustrate the model.

关键词： distributed problem solving Multiagent cooperative computation distributed control Reasoning mechanisms and modes Memory recall model

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A distributed problem-solving framework for probabilistic software effort estimation

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EXPERT SYSTEMS 2012年第5期29卷 492-505页

作者： Pendharkar, Parag C. Rodger, James A. Penn State Harrisburg Sch Business Adm Informat Syst Middletown PA 17057 USA Indiana Univ Penn Eberly Coll Business & Informat Technol Indiana PA 15705 USA

distributed problem-solving (DPS) systems use a framework of human organizational notions and principles of intelligent systems to solve complex problems. Human organizational notions are used to decompose a complex problem into sub-problems that can be solved using intelligent systems. The solutions of these sub-problems are combined to solve the original complex problem. In this paper, we propose a DPS system for probabilistic estimation of software development effort. Using a real-world software engineering dataset, we compare the performance of the DPS system with a neural network (NN) and show that the performance of the DPS system is equal to or better than that of the NN with the additional benefits of modularity, probabilistic estimates, greater interpretability, flexibility and capability to handle incomplete input data.

关键词： distributed problem solving software effort estimation probabilistic reasoning

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problem DECOMPOSITION IN distributed problem-solving SYSTEMS

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APPLIED INTELLIGENCE 1993年第4期3卷 301-315页

作者： PAPATHEODOROU, C MAGIROU, V KIOUNTOUZIS, V 1. Department of Informatics Athens University of Economics and Business 76 Patission Str. 104 34 Athens Greece

distributed problem solving (DPS) is defined as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers (agents), each of them knowing how to execute only some of the necessary tasks. This approach considers the problem-solving process as occurring in three phases: problem decomposition, subproblem solution, and answer synthesis. In the problem decomposition phase, one has to determine which tasks will be executed by each agent and when. One of the key research questions in the problem decomposition process is how to decompose a problem in order to minimize the cost of resources needed for its solution. In this article, we construct mathematical programming models in order to describe the decomposition process under the above criterion, study its complexity, and present exact and heuristic algorithms for its solution. Our work was motivated by the operation of an actual system that can be considered as a distributed problem solver for the assessment of irrigation projects design.

关键词： distributed problem solving problem DECOMPOSITION NP-COMPLETENESS DIRECTED ACYCLIC GRAPHS

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ANALYSIS OF COSMIC-RAY TRACKS USING distributed problem-solving

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PATTERN RECOGNITION LETTERS 1986年第6期4卷 471-476页

作者： DODHIAWALA, RT CROSS, GR WASHINGTON STATE UNIV DEPT COMP SCIPULLMANWA 99164

We describe an expert image analysis system, based on hearsay-iii, for the analysis of cosmic ray data using point pattern matching algorithms as knowledge sources.

关键词： Point pattern matching distributed problem solving blackboard systems artificial intelligence

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A Formal Approach for Designing distributed Expert problem-solving Systems

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INFORMATION SYSTEMS RESEARCH 1993年第2期4卷 141-165页

作者： De, Prabuddha Jacob, Varghese S. Pakath, Ramakrishnan Univ Dayton Dept Management Informat Syst & Decis Sci Dayton OH 45469 USA Ohio State Univ Dept Accounting & Management Informat Columbus OH 43210 USA Univ Kentucky Dept Decis Sci & Infromat Syst Lexington KY 40506 USA

In this paper, we consider the problem of generating effective information gathering, communication, and decision-making (ICD) strategies for a distributed expert problem-solving (DEPS) system. We focus on the special case of a dual-processor DEPS system and present a decision-theoretic model that enables the characterization of feasible, efficient, and optimal ICD strategies. In view of the tremendous amount of computing needed to generate optimal strategies for problems of practical size, we develop useful heuristic procedures for constructing high-quality efficient ICD strategies. We illustrate the use of the model and the solution procedure through an example.

关键词： Expert systems distributed problem solving Economic decision theory Information-gathering communication and decision-making strategies Computational complexity

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A distributed problem-solving INFRASTRUCTURE FOR COMPUTER NETWORK MANAGEMENT

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International Journal of Cooperative Information Systems 1992年第2期1卷 363-392页

作者： YOUNG-PA SO EDMUND H. DURFEE EECS Department The University of Michigan 1101 Beal Ave. Ann Arbor. MI 48109 USA

A distributed computer network management system consisting of cooperating autonomous computing agents allows network management to be more responsive due to information gathering and network recovery activities being performed in parallel. However, to perform these tasks, the network of agents requires a stable organizational infrastructure. In addition, to meet the needs of human network administrators, the distributed system must allow ultimate authority to be centralized at a single location. distributed Big Brother (DBB) represents a pragmatic blending of diverse technologies from the field of distributed AI, such as contract formation, organizational structuring, election for role assignment, and hierarchical control. The result is an infrastructure for a network management system in which separate agents reconfigure themselves when hardware and software failures occur in order to assure the authority structure demanded by network operators. Our efforts illustrate how integrating existing distributed AI technologies can meet realistic needs, and highlight open problems that require the development of new technologies.

关键词： distributed artificial intelligence distributed problem solving computer network management organizational self-design dynamic role distribution

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Coordinated problem solving through resource sharing in a distributed environment

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2004年第2期34卷 1299-1304页

作者： Deshpande, U Gupta, A Basu, A Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

An important feature in a distributed problem solving system is that the resources of different nodes can be shared through cooperation. In this paper, the generalized partial global planning (GPGP) approach used for multiagent systems is extended by providing a coordination mechanism for resource sharing across nodes. In our framework, multiple conflicting criteria (or objectives) like quality, cost, and duration may be associated with an input task. Preference ratings expressed subjectively may be assigned to each of the criteria. Task assignment in this system, which is a multiobjective decision making problem, is important for the satisfaction of the criteria. It has to be done with imprecise information since the system is dynamic and preference ratings are specified subjectively. A,technique for task assignment using the fuzzy set approach is also presented in this paper. Simulation studies for the coordination mechanism and the task assignment have been performed to demonstrate their effectiveness.

关键词： contract net protocol distributed problem solving fuzzy logic multiagent systems multiobjective decision making

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Separate but equal: Equality in belief propagation for single-cycle graphs

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ARTIFICIAL INTELLIGENCE 2025年 338卷

作者： Cohen, Erel Rachmut, Ben Lev, Omer Zivan, Roie Ben Gurion Univ Negev Dept Ind Engn & Management Beer Sheva Israel

Belief propagation is a widely used, incomplete optimization algorithm whose main theoretical properties hold only under the assumption that beliefs are not equal. Nevertheless, there is substantial evidence to suggest that equality between beliefs does occur. A published method to overcome belief equality, which is based on the use of unary function-nodes, is commonly assumed to resolve the problem. In this study, we focus on min-sum, the version of belief propagation that is used to solve constraint optimization problems. We prove that for the case of a single-cycle graph, belief equality can only be avoided when the algorithm converges to the optimal solution. Under any other circumstances, the unary function method will not prevent equality, indicating that some of the existing results presented in the literature are in need of reassessment. We differentiate between belief equality, which refers to equal beliefs in a single message, and assignment equality, which prevents the coherent assignment of values to the variables, and we provide conditions for both.

关键词： Belief propagation distributed constraints distributed problem solving

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DKRL — AN OBJECT-ORIENTED LANGUAGE TO SUPPORT problem solving IN distributed BLACKBOARD SYSTEMS

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International Journal of Pattern Recognition and Artificial Intelligence 1993年第2期7卷 221-242页

作者： MANOJ K. SAXENA K.K. BISWAS P.C.P. BHATT Tecknowledge Consultants Shinghal Building Sadar Bazar Delhi Cantt-110010 India Computer Science and Engineering Department Indian Institute of Technology New Delhi-110016 India

For distributed problem solving systems, there is a need to define knowledge at two levels, one external to the agents and the other internal to the agents. External knowledge is required to achieve cooperation among agents and global convergence of the problem solving process, whereas internal knowledge is required to solve the sub-problems assigned to the agents. External knowledge specifies the role of each agent and its relationship with other agents in the system. Internal knowledge specifies knowledge structure and the problem solving process within each agent. DKRL is an object-oriented language for describing distributed blackboard systems. In DKRL a problem solving system is described as a collection of distributed intelligent, autonomous agents (modelled as objects), cooperating to solve the problem. An agent consists of a knowledge base, a behaviour part, a local controller, a monitor, and a communication controller. DKRL has characteristics of a dynamic nature, i.e. the agents can be created dynamically and the relationship among them also changes dynamically. An agent in DKRL’s computational model cannot be activated by more than one message at the same time and uses a virtual synchrony environment for message transmission among agents. This model combines the advantages of remote procedure calls with those of asynchronous message passing. DKRL allows object-oriented programming techniques to be used for system development and facilitates the development by allowing one-to-one mapping between the objects in the knowledge model and the knowledge base of the agent. In this paper, we give an overview of the distributed blackboard paradigm for which DKRL was developed as well as the design considerations. We also propose and formally describe the underlying models of DKRL and explain how concurrency is exploited by DKRL. We conclude with the current status of and preliminary experience with DKRL in using it for the development of a gate assignment problem.

关键词： Blackboard systems distributed artificial intelligence distributed problem solving distributed systems object-oriented languages

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Speeding up distributed pseudo-tree optimization procedures with cross edge consistency to solve DCOPs

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APPLIED INTELLIGENCE 2021年第3期51卷 1733-1746页

作者： Rashik, Mashrur Rahman, Md Musfiqur Khan, Md Mosaddek Mamun-or-Rashid, Md Long Tran-Thanh Jennings, Nicholas R. Univ Dhaka Dept Comp Sci & Engn Dhaka Bangladesh Univ Southampton Sch Elect & Comp Sci Southampton Hants England Imperial Coll London Dept Comp London England Imperial Coll London Dept Elect & Elect Engn London England

The distributed Pseudo-tree Optimization Procedure (DPOP) is a well-known message passing algorithm that provides optimal solutions to distributed Constraint Optimization problems (DCOPs) in cooperative multi-agent systems. However, the traditional DCOP formulation does not consider constraints that must be satisfied (hard constraints), rather it concentrates only on constraints that place no restriction on satisfaction (soft constraints). This is a serious shortcoming as many real-world applications involve both types of constraints. Traditional DPOP algorithms are not able to benefit from the existence of hard constraints, where an additional calculation is required to handle such constraints. This results in longer runtimes. Thus scalability remains an issue. Additionally, in the standard DPOP, the agents are arranged as a Depth First Search (DFS) pseudo-tree, but recent work has shown that the construction of pseudo-trees in this way often leads to chain-like communication structures that greatly impair the algorithm's performance. To address these issues, we develop an algorithm that speeds up the DPOP algorithm by reducing the size of the messages exchanged and increases parallelism in the pseudo tree. For this purpose, initially, we improve the path for exchanging messages. Next, we introduce a new form of constraint propagation, which we call cross-edge consistency. Our theoretical evaluation shows that our proposed algorithm is complete and correct. In empirical evaluations, our algorithm achieves a significant reduction in the runtime, ranging from 4% to 96%, compared to the state-of-the-art.

关键词： Multiagent system distributed problem solving distributed constraint optimization DPOP

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