goal has been the development of adaptive, scalable, reusable
and distributed infrastructure for the flexible reconfiguration
of agent teams working with their human counterparts in intelligence
gathering, information retrieval and fusion, decision making and
planning--within rapidly changing contexts. Our work has focused
on agent and agent system interoperability, especially in connection
with the Grid. We have also developed the RETSINA (Reusable Environment
for Task-Structured Intelligent Networked Agents) Agent Infrastructure
and demonstrated the interoperation of the RETSINA Infrastructure
with other agent infrastructures.
team decision-making has become more important due to increased
decentralization of the military C2 process and the rapid restructuring
of joint commands to keep pace with new forms of conflict and
threats. Supporting collaboration and joint decision has become
more and more difficult in the face of shortening decision cycles
and new forms of warfare wherein the opponents are spatially distributed
and difficult to identify, and where tracking and coordination
of intelligence depends upon timely and effective access to distributed,
and often disparate information systems.
Our overall research hypothesis is that to effectively support teams in
information fusion and decision making in a uniform and interoperable
way, each team member and the team as a whole should be supported
by a distributed collection of intelligent agents that cooperate
to access and fuse information from distributed
heterogeneous information sources, monitor information
sources at many levels in a focused way, make inferences
based on information access, determine new information gathering
and fusion activities (e.g. new areas to search for threats, attacks
and/or targets) based on their inferences, and provide customized
views and alarms to the commanders and other members
of the defense team.
accordance with this vision, we have developed multi-agent technology
to support commanders and other defense team members with ubiquitous
computing in real-time joint decision-making under novel, uncertain,
and rapidly changing situations.
Our work on the Grid and multi-agent infrastructure (http://www2.cs.cmu.edu/~softagents/retsina.html)
is designed for decision support
in an open, unpredictable, and dynamic information environments.
User requests are appropriately and flexibly matched to information
sources, whether distributed over the Internet or in other accessible
information environments. We have designed flexible, extensible
means to locate information relevant to a task during task
execution, and to present information to a variety of devices
and platforms (http://www-2.cs.cmu.edu/~softagents/mocca.html). Agent to agent communications has been developed to
allow agents to autonomously find and semantically interoperate
with each other, across distributed systems.
RETSINA supports many different specialized agents that represent the user,
the decision task that the fused information is meant to support
(e.g. intent inferencing task), and the information resources.
In RETSINA, these agents are of the following general types:
Interface agents receive the
user requests and appropriately present information to the user.
These agents use knowledge of the domain and the user role and
functionality to help the user formulate and customize his/her
information requests. They also plan appropriate interactions
with other agents on the user’s behalf. Interface agents can also
learn to anticipate user information needs and proactively
pre-position information that the user has been observed in the
past to need. Agents can also interface with “agentified” devices
so as to negotiate the best way to present/acquire information
for the human users of the agent system.
Task agents have knowledge of
the task domain and also have planning abilities. Utilizing the
task models, and with planning, they are able to plan for a specific
information gathering goal or an inferencing task. For example,
they can decompose a high level information request (e.g.
“find the state of readiness of a platoon”) to lower level tasks,
and form plans for how to execute the information gathering
subtasks, find (through middle agents) and query
the appropriate information sources, and coordinate the query
execution and composition of the query results. Task
agents use planning mechanisms that combine planning and execution
of the information gathering and inferencing tasks.
adaptively considers the current operational environment and the
potential for changing resources. For example, some information
sources may be present at one time but absent at another time
(e.g., the source or its communication link could be damaged after
an attack or during battle). Planning includes providing back-up
information sources available for such contingencies.
Information agents: each information
agent wraps an information source and knows the particular
details of how to interact with the source to answer a query.
In addition to one-time access to information, an information
agent can be given event monitoring and notification triggers
called monitoring queries. For example, “provide me the
status of X facility every 5 minutes”, or “provide me the status
of X facility” if particular conditions obtain.
provide varying degrees of semantic interoperability. They act
as intelligent registries of agents. They allow service/agent
discovery and lookup by semantically matching information
needs of requester agents (or humans) with available information
resources so that the requests can be routed to the available
resources. The most popular Middle Agents are Brokers, Facilitators,
and Matchmakers. We have identified 28 different types of Middle
Agents and have experimented with different performance characteristics.
Any agent or agent type can produce
results that are potentially useful to other agents and agent
types. Thus, any agent may wish to advertise its capabilities
with a middle agent, so that it can be found and its services
accessed. This capability-based coordination contributes
to the discovery and semantic interoperation of agents, which
is one of the most crucial challenges facing the transformation
of the Internet into a Servicenet.
Katia Sycara, PI
at cs dot cmu dot edu
at cs dot cmu dot edu