9 research outputs found
Arguing Using Opponent Models
Peer reviewedPostprin
Learning in Multi-Agent Information Systems - A Survey from IS Perspective
Multiagent systems (MAS), long studied in artificial intelligence, have recently become popular in mainstream IS research. This resurgence in MAS research can be attributed to two phenomena: the spread of concurrent and distributed computing with the advent of the web; and a deeper integration of computing into organizations and the lives of people, which has led to increasing collaborations among large collections of interacting people and large groups of interacting machines. However, it is next to impossible to correctly and completely specify these systems a priori, especially in complex environments. The only feasible way of coping with this problem is to endow the agents with learning, i.e., an ability to improve their individual and/or system performance with time. Learning in MAS has therefore become one of the important areas of research within MAS. In this paper we present a survey of important contributions made by IS researchers to the field of learning in MAS, and present directions for future research in this area
Existence of Multiagent Equilibria with Limited Agents
Multiagent learning is a necessary yet challenging problem as multiagent
systems become more prevalent and environments become more dynamic. Much of the
groundbreaking work in this area draws on notable results from game theory, in
particular, the concept of Nash equilibria. Learners that directly learn an
equilibrium obviously rely on their existence. Learners that instead seek to
play optimally with respect to the other players also depend upon equilibria
since equilibria are fixed points for learning. From another perspective,
agents with limitations are real and common. These may be undesired physical
limitations as well as self-imposed rational limitations, such as abstraction
and approximation techniques, used to make learning tractable. This article
explores the interactions of these two important concepts: equilibria and
limitations in learning. We introduce the question of whether equilibria
continue to exist when agents have limitations. We look at the general effects
limitations can have on agent behavior, and define a natural extension of
equilibria that accounts for these limitations. Using this formalization, we
make three major contributions: (i) a counterexample for the general existence
of equilibria with limitations, (ii) sufficient conditions on limitations that
preserve their existence, (iii) three general classes of games and limitations
that satisfy these conditions. We then present empirical results from a
specific multiagent learning algorithm applied to a specific instance of
limited agents. These results demonstrate that learning with limitations is
feasible, when the conditions outlined by our theoretical analysis hold
Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems
Much research in artificial intelligence is concerned with the development of
autonomous agents that can interact effectively with other agents. An important
aspect of such agents is the ability to reason about the behaviours of other
agents, by constructing models which make predictions about various properties
of interest (such as actions, goals, beliefs) of the modelled agents. A variety
of modelling approaches now exist which vary widely in their methodology and
underlying assumptions, catering to the needs of the different sub-communities
within which they were developed and reflecting the different practical uses
for which they are intended. The purpose of the present article is to provide a
comprehensive survey of the salient modelling methods which can be found in the
literature. The article concludes with a discussion of open problems which may
form the basis for fruitful future research.Comment: Final manuscript (46 pages), published in Artificial Intelligence
Journal. The arXiv version also contains a table of contents after the
abstract, but is otherwise identical to the AIJ version. Keywords: autonomous
agents, multiagent systems, modelling other agents, opponent modellin
Self–organised multi agent system for search and rescue operations
Autonomous multi-agent systems perform inadequately in time critical missions, while they tend to
explore exhaustively each location of the field in one phase with out selecting the pertinent strategy. This
research aims to solve this problem by introducing a hierarchy of exploration strategies. Agents explore
an unknown search terrain with complex topology in multiple predefined stages by performing pertinent
strategies depending on their previous observations. Exploration inside unknown, cluttered, and confined
environments is one of the main challenges for search and rescue robots inside collapsed buildings. In
this regard we introduce our novel exploration algorithm for multi–agent system, that is able to perform
a fast, fair, and thorough search as well as solving the multi–agent traffic congestion.
Our simulations have been performed on different test environments in which the complexity of the
search field has been defined by fractal dimension of Brownian movements. The exploration stages are
depicted as defined arenas of National Institute of Standard and Technology (NIST). NIST introduced
three scenarios of progressive difficulty: yellow, orange, and red. The main concentration of this research
is on the red arena with the least structure and most challenging parts to robot nimbleness
Model-based Learning of Interaction Strategies in Multi-agent Systems
Agents that operate in a multi-agent system need an efficient strategy to handle their encounters with other agents involved. Searching for an optimal interaction strategy is a hard problem because it depends mostly on the behavior of the others. One way to deal with this problem is to endow the agents with the ability to adapt their strategies based on their interaction experience. This work views interaction as a repeated game and presents a general architecture for a model-based agent that learns models of the rival agents for exploitation in future encounters. First, we describe a method for inferring an optimal strategy against a given model of another agent. Second, we present an unsupervised algorithm that infers a model of the opponent's strategy from its interaction behavior in the past. We then present a method for incorporating exploration strategies into model-based learning. We report experimental results demonstrating the superiority of the model-based learning agent over..