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Current Projects
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Organizational
design, performance and adaptation
| about |
We develop KB-ORG: a fully automated, knowledge-basedorganization designer
for multi-agent systems. KB-ORG uses both application-level and
coordination-level organization design knowledge to explore the combinatorial
search space of candidate organizations selectively. We also show that KB-ORG
designs effective, yet substantially different, organizations when given
different requirements and environmental expectations. |
| people |
Mark Sims, Dan Corkill, Victor Lesser |
Complex
negotiation models in electronic commerce
| about |
In electronic commerce markets where selfish agents behave individually,
agents often have incomplete information as well as many constraints like
budget, deadline. We are developing negotiation models in which agents often
have to acquire multiple resources in order to accomplish a high level task with
each resource acquisition requiring a separate negotiation thread. We take a set
of elements into account, for example, deadline, outside option, market
competition, multiple resources, and decommitment. |
| people |
Bo An, Victor Lesser |
Approximately
solving sequential games
| about |
Games of incomplete information are notorious for their difficulty which
usually makes finding an exact solution intractable. The problem is even harder
when the game has multiple stages and sequential decision-making is required. We
developed an anytime search algorithm for calculating approximate Bayes-Nash
equilibria in sequential games of incomplete information. Experimental results
demonstrate our algorithm's attractive anytime behavior which allows it to find
good-enough solutions to large games within reasonable amounts of time. |
| people |
Hala Mostafa, Victor Lesser |
Distributed
Bayesian Networks
| about |
In environments where observations and reasoning are distributed, gathering
information consumes considerable resources. Moreover, having smaller networks
reduces reasoning complexity. Instead of breaking the large or distributed
network at random convenient points, understanding the relationships between
nodes by identifying necessary evidences for reasoning helps reduce the size of
the network. Context-specific independence provides grounds to identify a set of
observations necessary for accurate reasoning that is smaller than the network
structure itself. |
| people |
Yoonheui Kim, Victor Lesser |
Organizationally
motivated network routing
| about |
Multi-Agent organization knowledge is traditionally not utilized by
general-purpose wireless network routing algorithms normally used to support
agent communication. We show that incorporating organization knowledge
(otherwise available only to the application layer) in the network-layer routing
algorithm increases bandwidth available at the application layer. |
| people |
Huzaifa Zafar, Dan Corkill, Victor Lesser |
Learning
distributed task allocation policies
| about |
In the multi-agent setting, agents are simultaneously learning their
interaction policies, which can result in low convergence and even divergence of
the learning. We investigate the issue of scalable efficient Multi-Agent
Reinforcement Learning (MARL) techniques by introducing a hierarchical
organization with multi-leveled distributed supervision, and propose a general
approach to integrating this supervision mechanism with MARLs to coordinate
learning among agents. Each level in the organization structure is an overlay
network. |
| people |
Chongjie Zhang, Victor Lesser |
GILA: Integrated
Learning
| about | We study ways to coordinate multiple
learning agents, each with a different learning algorithm, and
integrate their hypothesis to cooperatively and incrementally solve
complex problems. We also investigate learning algorithms that can
improve such coordination and cooperation. Air traffic flight planning
is the primary application of this research project. | | people | Chongjie Zhang, Hala Mostafa, Dan Corkill, Victor Lesser |
CNAS - Collaborative Network for Atmospheric Sensing
| about |
CNAS is an
experimental, agent based, power-aware sensor network for ground-level
atmospheric monitoring. Due to characteristics such as sparse deployment of nodes, replenishable
energy sources, and a need for turning off WiFi radios for most of the
time, CNAS provides an interesting application for research in organizationally motivated routing,
developing solar energy harvesting models, and multi-agent systems deployment.
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| people |
Dan Corkill, Huzaifa Zafar |
| papers |
[AAMAS-08], [ATSN08(Zafar,Corkill)], [ATSN08(Corkill)], [ATSN07] |
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Research Sponsors
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| about |
Integrating contributions received from other agents is an essential
activity in multi-agent systems (MASs). Not only must related contributions be
integrated together, but the confidence in each integrated contribution must be
determined. We look specifically at the issue of confidence determination and
its effect on developing "principled" highly collaborating MASs. |
Past Projects
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Fusing contributions
from multiple agents
| about |
Integrating contributions received from other agents is an essential
activity in multi-agent systems (MASs). Not only must related contributions be
integrated together, but the confidence in each integrated contribution must be
determined. We look specifically at the issue of confidence determination and
its effect on developing "principled" highly collaborating MASs. |
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Autonomous Negotiating Teams
| about |
The Autonomous Negotiating Teams (ANTS) project seeks to address the problem
of coordinating over constrained resources in an uncertain, real-time domain. In
this domain, there are a number of radar based sensors that must track targets
moving through the environment. The sensors must coordinate their activity to
"triangulate" the exact position of the targets. Using a variety of techniques,
which has pushed our technology to the next level, we have developed a set of
negotiation strategies that operate on different levels of abstraction based on
the immediate demands of the environment to coordinate the use of the sensors.
Future work in this domain includes the development of methods for organizing
large-scale collections of agents. |
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Explaining the
Behavior of Multi-Agent Systems
| about |
As multi-agent systems grow in scale and complexity it is increasingly
difficult to understand and characterize the behavior of the system as a
whole as well as individual agents. In order to help investigators
understand these complex systems, we are developing analysis techniques
and software tools that are founded on analytic approaches developed
within the intelligence analysis and social network analysis
communities. Specifically, we are using relational data representations
that we believe can capture many of the important aspects of the
behavior of multi-agent systems, and potentially capture far more of
those behaviors than traditional data representations. These approaches
are almost unknown in computer science, but we believe that they are
uniquely suited to help understand the behavior of multi-agent systems. |
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Bounded Information Gathering
| about |
The BIG (resource-Bounded Information Gathering) project is an information
gathering agent that helps Internet shoppers cope with the proliferation of
electronically available information. The process begins when a user enters in a
desired product's characteristics, and search criteria. BIG then uses this
information to perform the actual search and recommendation process, which
includes generating and scheduling a plan of activities, searching through
natural language reviews and descriptions, sophisticated text processing,
reasoning about resource trade-offs, and the extraction and consolidation of
relevant information. Once completed, BIG presents the user with the list of
product descriptions which best satisfy the user's criteria. |
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Soft Real-Time Architecture
| about |
Real-time control has become increasingly important as technologies are
moved from the lab into real world situations or physical simulations. The
complexity associated with these systems increases as control and autonomy are
distributed, due to such issues as precedence constraints, shared resources, and
the lack of a complete and consistent world view. The Soft Real-Time
Architecture addresses these issues by providing a robust scheduling and
execution subsystem capable of quantitatively reasoning over deadlines and
resource constraints. This provides a useful layer of abstraction, enabling the
agent's higher level reasoning components to operate at a more tractable level
of granularity, without sacrificing fine-grained control and reactivity. |
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