76 research outputs found
An Axiomatic Approach to Routing
Information delivery in a network of agents is a key issue for large, complex
systems that need to do so in a predictable, efficient manner. The delivery of
information in such multi-agent systems is typically implemented through
routing protocols that determine how information flows through the network.
Different routing protocols exist each with its own benefits, but it is
generally unclear which properties can be successfully combined within a given
algorithm. We approach this problem from the axiomatic point of view, i.e., we
try to establish what are the properties we would seek to see in such a system,
and examine the different properties which uniquely define common routing
algorithms used today.
We examine several desirable properties, such as robustness, which ensures
adding nodes and edges does not change the routing in a radical, unpredictable
ways; and properties that depend on the operating environment, such as an
"economic model", where nodes choose their paths based on the cost they are
charged to pass information to the next node. We proceed to fully characterize
minimal spanning tree, shortest path, and weakest link routing algorithms,
showing a tight set of axioms for each.Comment: In Proceedings TARK 2015, arXiv:1606.0729
Cooperating intelligent systems
Some of the issues connected to the development of a bureaucratic system are discussed. Emphasis is on a layer multiagent approach to distributed artificial intelligence (DAI). The division of labor in a bureaucracy is considered. The bureaucratic model seems to be a fertile model for further examination since it allows for the growth and change of system components and system protocols and rules. The first part of implementing the system would be the construction of a frame based reasoner and the appropriate B-agents and E-agents. The agents themselves should act as objects and the E-objects in particular should have the capability of taking on a different role. No effort was made to address the problems of automated failure recovery, problem decomposition, or implementation. Instead what has been achieved is a framework that can be developed in several distinct ways, and which provides a core set of metaphors and issues for further research
Organisational Abstractions for the Analysis and Design of Multi-Agent Systems
The architecture of a multi-agent system can naturally be viewed as a computational organisation. For this reason, we believe organisational abstractions should play a central role in the analysis and design of such systems. To this end, the concepts of agent roles and role models are increasingly being used to specify and design multi-agent systems. However, this is not the full picture. In this paper we introduce three additional organisational concepts - organisational rules, organisational structures, and organisational patterns - that we believe are necessary for the complete specification of computational organisations. We view the introduction of these concepts as a step towards a comprehensive methodology for agent-oriented systems
A Generic Agent Organisation Framework For Autonomic Systems
Autonomic computing is being advocated as a tool for managing large, complex computing systems. Specifically, self-organisation provides a suitable approach for developing such autonomic systems by incorporating self-management and adaptation properties into large-scale distributed systems. To aid in this development, this paper details a generic problem-solving agent organisation framework that can act as a modelling and simulation platform for autonomic systems. Our framework describes a set of service-providing agents accomplishing tasks through social interactions in dynamically changing organisations. We particularly focus on the organisational structure as it can be used as the basis for the design, development and evaluation of generic algorithms for self-organisation and other approaches towards autonomic systems
Organization of Multi-Agent Systems: An Overview
In complex, open, and heterogeneous environments, agents must be able to
reorganize towards the most appropriate organizations to adapt unpredictable
environment changes within Multi-Agent Systems (MAS). Types of reorganization
can be seen from two different levels. The individual agents level
(micro-level) in which an agent changes its behaviors and interactions with
other agents to adapt its local environment. And the organizational level
(macro-level) in which the whole system changes it structure by adding or
removing agents. This chapter is dedicated to overview different aspects of
what is called MAS Organization including its motivations, paradigms, models,
and techniques adopted for statically or dynamically organizing agents in MAS.Comment: 12 page
Adaptive Load Balancing: A Study in Multi-Agent Learning
We study the process of multi-agent reinforcement learning in the context of
load balancing in a distributed system, without use of either central
coordination or explicit communication. We first define a precise framework in
which to study adaptive load balancing, important features of which are its
stochastic nature and the purely local information available to individual
agents. Given this framework, we show illuminating results on the interplay
between basic adaptive behavior parameters and their effect on system
efficiency. We then investigate the properties of adaptive load balancing in
heterogeneous populations, and address the issue of exploration vs.
exploitation in that context. Finally, we show that naive use of communication
may not improve, and might even harm system efficiency.Comment: See http://www.jair.org/ for any accompanying file
On Partially Controlled Multi-Agent Systems
Motivated by the control theoretic distinction between controllable and
uncontrollable events, we distinguish between two types of agents within a
multi-agent system: controllable agents, which are directly controlled by the
system's designer, and uncontrollable agents, which are not under the
designer's direct control. We refer to such systems as partially controlled
multi-agent systems, and we investigate how one might influence the behavior of
the uncontrolled agents through appropriate design of the controlled agents. In
particular, we wish to understand which problems are naturally described in
these terms, what methods can be applied to influence the uncontrollable
agents, the effectiveness of such methods, and whether similar methods work
across different domains. Using a game-theoretic framework, this paper studies
the design of partially controlled multi-agent systems in two contexts: in one
context, the uncontrollable agents are expected utility maximizers, while in
the other they are reinforcement learners. We suggest different techniques for
controlling agents' behavior in each domain, assess their success, and examine
their relationship.Comment: See http://www.jair.org/ for any accompanying file
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