Multi-agent Algorithm for Re-allocating Grid-resources and Improving Fault-tolerance of Problem-solving Processes

Decisions at different levels of the supply chain can no longer be considered independently, since they may influence profitability throughout the supply chain. This paper focuses on the interest of multi-agent paradigm for the collaborative coordination in global distribution supply chain. Multi-agent computational environments are suitable for a broad class of coordination and negotiation issues involving multiple autonomous or semiautonomous problem solving contexts. An agent-based distributed architecture is proposed for better management of rush unexpected orders. This paper proposes a first architecture validated by a real and industrial case.

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Society of Agents

Natural Language Processing ◽

10.4018/978-1-7998-0951-7.ch009 ◽

2020 ◽

pp. 160-183

Author(s):

Steven Walczak

Keyword(s):

Problem Solving ◽

Information Sharing ◽

Multi Agent Systems ◽

New Classification ◽

Conceptual Foundation ◽

Agent Systems ◽

Multi Agent ◽

Coordination And Collaboration ◽

Agent Coordination ◽

New Framework

The development of multiple agent systems faces many challenges, including agent coordination and collaboration on tasks. Minsky's The Society of Mind provides a conceptual view for addressing these multi-agent system problems. A new classification ontology is introduced for comparing multi-agent systems. Next, a new framework called the Society of Agents is developed from Minsky's conceptual foundation. A Society of Agents framework-based problem-solving and a Game Society is developed and applied to the domain of single player logic puzzles and two player games. The Game Society solved 100% of presented Sudoku and Kakuro problems and never lost a tic-tac-toe game. The advantage of the Society of Agents approach is the efficient re-utilization of agents across multiple independent game domain problems and a centralized problem-solving architecture with efficient cross-agent information sharing.

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A Multi-agent System with Smart Agents for Resource Allocation Problem Solving

10.1007/978-3-030-82199-9_53 ◽

2021 ◽

pp. 794-801

Author(s):

Alexey Goryashchenko

Keyword(s):

Resource Allocation ◽

Problem Solving ◽

Allocation Problem ◽

Resource Allocation Problem ◽

Multi Agent System ◽

Agent System ◽

Multi Agent

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Parallelism and Fault Tolerance

Multidimensional Programming ◽

10.1093/oso/9780195075977.003.0009 ◽

1995 ◽

Author(s):

E. A. Ashcroft ◽

A. A. Faustini ◽

R. Jaggannathan ◽

W. W. Wadge

Keyword(s):

Fault Tolerance ◽

Problem Solving ◽

Independent Functions

The intensional nature of Lucid and eduction has two important practical consequences: (i) Lucid programs possess massive amounts of implicit parallelism and (ii) their evaluation can automatically tolerate faults. This chapter is devoted to explaining these two consequences. We start with massive implicit parallelism in Lucid programs. There are three forms of parallelism that arise in problem solving [13, 6]. The simplest form of parallelism, functional parallelism, is in the simultaneous execution of independent functions (or operators). This is sometimes referred to as structural parallelism or static parallelism.

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