2,730 research outputs found
Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies
This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points
Towards formal models and languages for verifiable Multi-Robot Systems
Incorrect operations of a Multi-Robot System (MRS) may not only lead to
unsatisfactory results, but can also cause economic losses and threats to
safety. These threats may not always be apparent, since they may arise as
unforeseen consequences of the interactions between elements of the system.
This call for tools and techniques that can help in providing guarantees about
MRSs behaviour. We think that, whenever possible, these guarantees should be
backed up by formal proofs to complement traditional approaches based on
testing and simulation.
We believe that tailored linguistic support to specify MRSs is a major step
towards this goal. In particular, reducing the gap between typical features of
an MRS and the level of abstraction of the linguistic primitives would simplify
both the specification of these systems and the verification of their
properties. In this work, we review different agent-oriented languages and
their features; we then consider a selection of case studies of interest and
implement them useing the surveyed languages. We also evaluate and compare
effectiveness of the proposed solution, considering, in particular, easiness of
expressing non-trivial behaviour.Comment: Changed formattin
Multi Agent Systems in Logistics: A Literature and State-of-the-art Review
Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems
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Actions and Agents
In this chapter the notion of agency in AI is presented..It has been argued that in order to behave rationally in prevalent software applications artificial entities would have to be autonomous and adaptive. Besides, rather than working with single, isolated systems the new trend in AI would need to focus on inherently social entities in the form of multi-agent systems.
The chapter begins by introducing the notion of action in traditional AI systems, deliberative and reactive. Next, the idea of agency is presented as complementary to classical AI approaches to action as well as a key factor in the development of applications and services that currently rely on technologies where software systems execute instructions automatically. In particular, the chapter highlights the importance of developing theories of action and learning in multi-agent scenarios such as the Internet. This introduction shall conclude with some considerations about the research areas that need to be targeted if the agent paradigm is to become the standard in the design, specification and implementation of intelligent systems
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