TY - GEN

T1 - Collaborative Multi Agent Physical Search with Probabilistic Knowledge

AU - Hazon, Noam

AU - Aumann, Y.

AU - Kraus, S.

N1 - Place of conference:USA

PY - 2009

Y1 - 2009

N2 - This paper considers the setting wherein a group of
agents (e.g., robots) is seeking to obtain a given tangible
good, potentially available at different locations
in a physical environment. Traveling between
locations, as well as acquiring the good at any given
location consumes from the resources available to
the agents (e.g., battery charge). The availability of
the good at any given location, as well as the exact
cost of acquiring the good at the location is not
fully known in advance, and observed only upon
physically arriving at the location. However, apriori
probabilities on the availability and potential
cost are provided. Given such as setting, the
problem is to find a strategy/plan that maximizes
the probability of acquiring the good while minimizing
resource consumption. Sample applications
include agents in exploration and patrol missions,
e.g., rovers on Mars seeking to mine a specific mineral.
Although this model captures many real world
scenarios, it has not been investigated so far.
We focus on the case where locations are aligned
along a path, and study several variants of the problem,
analyzing the effects of communication and
coordination. For the case that agents can communicate,
we present a polynomial algorithm that
works for any fixed number of agents. For noncommunicating
agents, we present a polynomial algorithm
that is suitable for any number of agents.
Finally, we analyze the difference between homogeneous
and heterogeneous agents, both with respect
to their allotted resources and with respect to
their capabilities.

AB - This paper considers the setting wherein a group of
agents (e.g., robots) is seeking to obtain a given tangible
good, potentially available at different locations
in a physical environment. Traveling between
locations, as well as acquiring the good at any given
location consumes from the resources available to
the agents (e.g., battery charge). The availability of
the good at any given location, as well as the exact
cost of acquiring the good at the location is not
fully known in advance, and observed only upon
physically arriving at the location. However, apriori
probabilities on the availability and potential
cost are provided. Given such as setting, the
problem is to find a strategy/plan that maximizes
the probability of acquiring the good while minimizing
resource consumption. Sample applications
include agents in exploration and patrol missions,
e.g., rovers on Mars seeking to mine a specific mineral.
Although this model captures many real world
scenarios, it has not been investigated so far.
We focus on the case where locations are aligned
along a path, and study several variants of the problem,
analyzing the effects of communication and
coordination. For the case that agents can communicate,
we present a polynomial algorithm that
works for any fixed number of agents. For noncommunicating
agents, we present a polynomial algorithm
that is suitable for any number of agents.
Finally, we analyze the difference between homogeneous
and heterogeneous agents, both with respect
to their allotted resources and with respect to
their capabilities.

UR - https://scholar.google.co.il/scholar?q=Collaborative+Multi+Agent+Physical+Search+with+Probabilistic+Knowledge&btnG=&hl=en&as_sdt=0%2C5

M3 - Conference contribution

BT - IJCAI

ER -