MIT Encyclopedia of the Cognitive Sciences - Cryptome

MRI
See MAGNETIC RESONANCE IMAGING
Multiagent Systems
Multiagent systems are distributed computer systems in
which the designers ascribe to component modules autonomy,
mental state, and other characteristics of agency. Software
developers have applied multiagent systems to solve
problems in power management, transportation scheduling,
and a variety of other tasks. With the growth of the Internet
and networked information systems generally, separately
designed and constructed programs increasingly need to
interact substantively; such complexes also constitute multiagent
systems.
In the study of multiagent systems, including the field of
“distributed AI” (Bond and Gasser 1988) and much of the
current activity in “software agents” (Huhns and Singh
1997), researchers aim to relate aggregate behavior of the
composite system with individual behaviors of the component
agents and properties of the interaction protocol and
environment. Frameworks for constructing and analyzing
multiagent systems often draw on metaphors—as well as
models and theories—from the social and ecological sciences
(Huberman 1988). Such social conceptions are sometimes
applied within an agent to describe its behaviors in
terms of interacting subagents, as in Minsky’s society of
mind theory (Minsky 1986).
Design of a distributed system typically focuses on the
interaction mechanism—specification of agent communication
languages and interaction protocols. The interaction
mechanism generally includes means to implement decisions
or agreements reached as a function of the agents’
interactions. Depending on the context, developers of a distributed
system may also control the configuration of participating
agents, the INTELLIGENT AGENT ARCHITECTURE, or
even the implementation of agents themselves. In any case,
principled design of the interaction mechanism requires
some model of how agents behave within the mechanism,
and design of agents requires a model of the mechanism
rules, and (sometimes) models of the other agents.
One fundamental characteristic that bears on design of
interaction mechanisms is whether the agents are presumed
to be cooperative, which in the technical sense used here
means that they have the same objectives (they may have
heterogeneous capabilities, and may also differ on beliefs
and other agent attitudes). In a cooperative setting, the role
of the mechanism is to coordinate local decisions and disseminate
local information in order to promote these global
objectives. At one extreme, the mechanism could attempt to
centralize the system by directing each agent to transmit its
local state to a central source, which then treats its problem
as a single-agent decision. This approach may be infeasible
or expensive, due to the difficulty of aggregating belief
states, increased complexity of scale, and the costs and
delays of communication. Solving the problem in a decentralized
manner, in contrast, forces the designer to deal
Multiagent Systems 573
directly with issues of reconciling inconsistent beliefs and
accommodating local decisions made on the basis of partial,
conflicting information (Durfee, Lesser, and Corkill 1992).
Even among cooperative agents, negotiation is often necessary
to reach joint decisions. Through a negotiation process,
for example, agents can convey the relevant information
about their local knowledge and capabilities necessary
to determine a principled allocation of resources or tasks
among them. In the contract net protocol and its variants,
agents submit “bids” describing their abilities to perform
particular tasks, and a designated contract manager assigns
tasks to agents based on these bids. When tasks are not easily
decomposable, protocols for managing shared information
in global memory are required. Systems based on a
blackboard architecture use this global memory both to
direct coordinated actions of the agents and to share intermediate
results relevant to multiple tasks.
In a noncooperative setting, objectives as well as beliefs
and capabilities vary across agents. Noncooperative systems
are the norm when agents represent the interests of disparate
humans or human organizations. Note that having distinct
objectives does not necessarily mean that the agents are
adversarial or even averse to cooperation. It merely means
that agents cooperate exactly when they determine that it is
in their individual interests to do so.
The standard assumption for noncooperative multiagent
systems is that agents behave according to principles of
RATIONAL DECISION MAKING. That is, each agent acts to further
its individual objectives (typically characterized in
terms of UTILITY THEORY), subject to its beliefs and capabilities.
In this case, the problem of designing an interaction
mechanism corresponds to the standard economic concept
of mechanism design, and the mathematical tools of GAME
THEORY apply. Much current work in multiagent systems is
devoted to game-theoretic analyses of interaction mechanisms,
and especially negotiation protocols applied within
such mechanisms (Rosenschein and Zlotkin 1994). Economic
concepts expressly drive the design of multiagent
interaction mechanisms based on market price systems
(Clearwater 1996).
Both cooperative and noncooperative agents may derive
some benefit by reasoning expressly about the other agents.
Cooperative agents may be able to propose more effective
joint plans if they know the capabilities and intentions of the
other agents. Noncooperative agents can improve their bargaining
positions through awareness of the options and preferences
of others (agents that exploit such bargaining power
are called “strategic”; those that neglect to do so are “competitive”).
Because direct knowledge of other agents may be
difficult to come by, agents typically induce their models of
others from observations (e.g., “plan recognition”), within
an interaction or across repeated interactions.
See also AI AND EDUCATION; COGNITIVE ARTIFACTS;
HUMAN-COMPUTER INTERACTION; RATIONAL AGENCY
—Michael P. Wellman
References
Bond, A. H., and L. Gasser, Eds. (1988). Readings in Distributed
Artificial Intelligence. San Francisco: Kaufmann.