TY - GEN
T1 - Plan explanations as model reconciliation
T2 - 26th International Joint Conference on Artificial Intelligence, IJCAI 2017
AU - Chakraborti, Tathagata
AU - Sreedharan, Sarath
AU - Zhang, Yu
AU - Kambhampati, Subbarao
N1 - Funding Information:
This research is supported in part by the ONR grants N00014161-2892, N00014-13-1-0176, N00014-13-1-0519, N00014-15-1-2027, and the NASA grant NNX17AD06G.
PY - 2017
Y1 - 2017
N2 - When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a "model reconciliation problem" (MRP), where the AI system in effect suggests changes to the human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.
AB - When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a "model reconciliation problem" (MRP), where the AI system in effect suggests changes to the human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.
UR - http://www.scopus.com/inward/record.url?scp=85031946471&partnerID=8YFLogxK
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U2 - 10.24963/ijcai.2017/23
DO - 10.24963/ijcai.2017/23
M3 - Conference contribution
AN - SCOPUS:85031946471
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 156
EP - 163
BT - 26th International Joint Conference on Artificial Intelligence, IJCAI 2017
A2 - Sierra, Carles
PB - International Joint Conferences on Artificial Intelligence
Y2 - 19 August 2017 through 25 August 2017
ER -