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Agile Planning for Real-World Disaster Response

Agile Planning for Real-World Disaster Response
Agile Planning for Real-World Disaster Response
We consider a setting where an agent-based planner
instructs teams of human emergency responders to
perform tasks in the real world. Due to uncertainty
in the environment and the inability of the planner
to consider all human preferences and all attributes
of the real-world, humans may reject plans
computed by the agent. A na¨?ve solution that replans
given a rejection is inefficient and does not
guarantee the new plan will be acceptable. Hence,
we propose a new model re-planning problem using
a Multi-agent Markov Decision Process that
integrates potential rejections as part of the planning
process and propose a novel algorithm to efficiently
solve this new model. We empirically evaluate
our algorithm and show that it outperforms
current benchmarks. Our algorithm is also shown
to perform better in pilot studies with real humans.
132-138
Wu, Feng
034d274d-560b-4ee4-bcfd-c553079742ed
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Jiang, Wenchao
c93f05be-0fe0-4f1f-b8d6-326001d8edb0
Fischer, Joel
a320ad79-0fb5-464b-9eac-f74918b5ea68
Rodden, Tom
b7d2e320-3783-4d67-93ff-c7b29dd8ba8e
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Wu, Feng
034d274d-560b-4ee4-bcfd-c553079742ed
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Jiang, Wenchao
c93f05be-0fe0-4f1f-b8d6-326001d8edb0
Fischer, Joel
a320ad79-0fb5-464b-9eac-f74918b5ea68
Rodden, Tom
b7d2e320-3783-4d67-93ff-c7b29dd8ba8e
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Wu, Feng, Ramchurn, Sarvapali, Jiang, Wenchao, Fischer, Joel, Rodden, Tom and Jennings, Nicholas R. (2015) Agile Planning for Real-World Disaster Response. At International Joint Conference on Artificial Intelligence International Joint Conference on Artificial Intelligence. pp. 132-138.

Record type: Conference or Workshop Item (Paper)

Abstract

We consider a setting where an agent-based planner
instructs teams of human emergency responders to
perform tasks in the real world. Due to uncertainty
in the environment and the inability of the planner
to consider all human preferences and all attributes
of the real-world, humans may reject plans
computed by the agent. A na¨?ve solution that replans
given a rejection is inefficient and does not
guarantee the new plan will be acceptable. Hence,
we propose a new model re-planning problem using
a Multi-agent Markov Decision Process that
integrates potential rejections as part of the planning
process and propose a novel algorithm to efficiently
solve this new model. We empirically evaluate
our algorithm and show that it outperforms
current benchmarks. Our algorithm is also shown
to perform better in pilot studies with real humans.

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More information

Accepted/In Press date: April 2015
Published date: July 2015
Venue - Dates: International Joint Conference on Artificial Intelligence, 2015-04-01
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 377186
URI: https://eprints.soton.ac.uk/id/eprint/377186
PURE UUID: b5c04331-22e4-4fa6-8828-b7bbae66b395
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 17 May 2015 17:59
Last modified: 06 Jun 2018 12:42

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Contributors

Author: Feng Wu
Author: Sarvapali Ramchurn ORCID iD
Author: Wenchao Jiang
Author: Joel Fischer
Author: Tom Rodden
Author: Nicholas R. Jennings

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