TY - GEN

T1 - Team member reallocation via tree pruning

AU - Agmon, N.

AU - Kaminka, G.

AU - Kraus, S.

N1 - Place of conference:Pittsburgh, Pennsylvania, US

PY - 2005

Y1 - 2005

N2 - This paper considers the task reallocation problem, where k
agents are to be extracted from a coordinated group of N
agents in order to perform a new task. The interaction between
the team members and the cost associated with this
interaction are represented by a weighted graph. Consider a
group of N robots organized in a formation, the graph is the
monitoring graph which represents the sensorial capabilities
of the robots, i.e., which robot can sense the other and at what
cost. Following this example, the team member reallocation
problem this paper deals with is the extraction of k robots
from the group in order to acquire a new target, while minimizing
the cost of the interaction of the remaining group. In
general, the method proposed here shifts the utility from the
team member itself to the interaction between the members,
and calculates the reallocation according to this interaction
utility. We found that this can be done optimally by a deterministic
polynomial time algorithm under several constraints,
the first constraint is that k = O(log N). We describe several
other domains in which this method is applicable.

AB - This paper considers the task reallocation problem, where k
agents are to be extracted from a coordinated group of N
agents in order to perform a new task. The interaction between
the team members and the cost associated with this
interaction are represented by a weighted graph. Consider a
group of N robots organized in a formation, the graph is the
monitoring graph which represents the sensorial capabilities
of the robots, i.e., which robot can sense the other and at what
cost. Following this example, the team member reallocation
problem this paper deals with is the extraction of k robots
from the group in order to acquire a new target, while minimizing
the cost of the interaction of the remaining group. In
general, the method proposed here shifts the utility from the
team member itself to the interaction between the members,
and calculates the reallocation according to this interaction
utility. We found that this can be done optimally by a deterministic
polynomial time algorithm under several constraints,
the first constraint is that k = O(log N). We describe several
other domains in which this method is applicable.

UR - http://www.aaai.org/Papers/AAAI/2005/AAAI05-006.pdf

M3 - Conference contribution

BT - 20th National Conference on Artifcial Intelligence

ER -