TY - GEN
T1 - Balancing explicability and explanations for human-aware planning
AU - Chakraborti, Tathagata
AU - Sreedharan, Sarath
AU - Kambhampati, Subbarao
N1 - Funding Information:
Majority of this work was done when all the authors were at Arizona State University. This research is supported in part by ONR grants N00014-16-1-2892, N00014-18-1-2442, and N00014-18-1-2840, AFOSR grant FA9550-18-1-0067 and NASA grant NNX17AD06G.
Publisher Copyright:
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Human-aware planning involves generating plans that are explicable as well as providing explanations when such plans cannot be found. In this paper, we bring these two concepts together and show how an agent can achieve a trade-off between these two competing characteristics of a plan. In order to achieve this, we conceive a first of its kind planner MEGA that can augment the possibility of explaining a plan in the plan generation process itself. We situate our discussion in the context of recent work on explicable planning and explanation generation, and illustrate these concepts in two well-known planning domains, as well as in a demonstration of a robot in a typical search and reconnaissance task. Human factor studies in the latter highlight the usefulness of the proposed approach.
AB - Human-aware planning involves generating plans that are explicable as well as providing explanations when such plans cannot be found. In this paper, we bring these two concepts together and show how an agent can achieve a trade-off between these two competing characteristics of a plan. In order to achieve this, we conceive a first of its kind planner MEGA that can augment the possibility of explaining a plan in the plan generation process itself. We situate our discussion in the context of recent work on explicable planning and explanation generation, and illustrate these concepts in two well-known planning domains, as well as in a demonstration of a robot in a typical search and reconnaissance task. Human factor studies in the latter highlight the usefulness of the proposed approach.
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M3 - Conference contribution
AN - SCOPUS:85074904318
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 1335
EP - 1343
BT - Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
A2 - Kraus, Sarit
PB - International Joint Conferences on Artificial Intelligence
T2 - 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Y2 - 10 August 2019 through 16 August 2019
ER -