TY - GEN
T1 - Goal recognition design
AU - Keren, Sarah
AU - Gal, Avigdor
AU - Karpas, Erez
N1 - Publisher Copyright:
Copyright © 2014, Association for the Advancement of Artificial Intelligence.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - We propose a new problem we refer to as goal recognition design (grd), in which we take a domain theory and a set of goals and ask the following questions: to what extent do the actions performed by an agent within the model reveal its objective, and what is the best way to modify a model so that any agent acting in the model reveals its objective as early as possible. Our contribution is the introduction of a new measure we call worst case distinctiveness (wed) with which we assess a grd model. The wed represents the maximal length of a prefix of an optimal path an agent may take within a system before it becomes clear at which goal it is aiming. To model and solve the grd problem we choose to use the models and tools from the closely related field of automated planning. We present two methods for calculating the wed of a grd model, one of which is based on a novel compilation to a classical planning problem. We then propose a way to reduce the wed of a model by limiting the set of available actions an agent can perform and provide a method for calculating the optimal set of actions to be removed from the model. Our empirical evaluation shows the proposed solution to be effective in computing and minimizing wed.
AB - We propose a new problem we refer to as goal recognition design (grd), in which we take a domain theory and a set of goals and ask the following questions: to what extent do the actions performed by an agent within the model reveal its objective, and what is the best way to modify a model so that any agent acting in the model reveals its objective as early as possible. Our contribution is the introduction of a new measure we call worst case distinctiveness (wed) with which we assess a grd model. The wed represents the maximal length of a prefix of an optimal path an agent may take within a system before it becomes clear at which goal it is aiming. To model and solve the grd problem we choose to use the models and tools from the closely related field of automated planning. We present two methods for calculating the wed of a grd model, one of which is based on a novel compilation to a classical planning problem. We then propose a way to reduce the wed of a model by limiting the set of available actions an agent can perform and provide a method for calculating the optimal set of actions to be removed from the model. Our empirical evaluation shows the proposed solution to be effective in computing and minimizing wed.
UR - https://www.scopus.com/pages/publications/84933049472
U2 - 10.1609/icaps.v24i1.13617
DO - 10.1609/icaps.v24i1.13617
M3 - Conference contribution
AN - SCOPUS:84933049472
T3 - Proceedings International Conference on Automated Planning and Scheduling, ICAPS
SP - 154
EP - 162
BT - ICAPS 2014 - Proceedings of the 24th International Conference on Automated Planning and Scheduling
A2 - Chien, Steve
A2 - Fern, Alan
A2 - Ruml, Wheeler
A2 - Do, Minh
PB - Association for the Advancement of Artificial Intelligence
T2 - 24th International Conference on Automated Planning and Scheduling, ICAPS 2014
Y2 - 21 June 2014 through 26 June 2014
ER -