TY - JOUR
T1 - Sequential Plan Recognition
AU - Mirsky, Reuth
AU - Stern, Roni
AU - Gal, Ya'Akov Kobi
AU - Kalech, Meir
N1 - Funding Information:
This research was funded in part by ISF grant numbers 363/12 and 1276/12, and by EU FP7 FET project no. 60085. R.M. is a recipient of the Pratt fellowship at the Ben-Gurion University of the Negev
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Plan recognition algorithms infer agents' plans from their observed actions. Due to imperfect knowledge about the agent's behavior and the environment, it is often the case that there are multiple hypotheses about an agent's plans that are consistent with the observations, though only one of these hypotheses is correct. This paper addresses the problem of how to disambiguate between hypotheses, by querying the acting agent about whether a candidate plan in one of the hypotheses matches its intentions. This process is performed sequentially and used to update the set of possible hypotheses during the recognition process. The paper defines the sequential plan recognition process (SPRP), which seeks to reduce the number of hypotheses using a minimal number of queries. We propose a number of policies for the SPRP which use maximum likelihood and information gain to choose which plan to query. We show this approach works well in practice on two domains from the literature, significantly reducing the number of hypotheses using fewer queries than a baseline approach. Our results can inform the design of future plan recognition systems that interleave the recognition process with intelligent interventions of their users.
AB - Plan recognition algorithms infer agents' plans from their observed actions. Due to imperfect knowledge about the agent's behavior and the environment, it is often the case that there are multiple hypotheses about an agent's plans that are consistent with the observations, though only one of these hypotheses is correct. This paper addresses the problem of how to disambiguate between hypotheses, by querying the acting agent about whether a candidate plan in one of the hypotheses matches its intentions. This process is performed sequentially and used to update the set of possible hypotheses during the recognition process. The paper defines the sequential plan recognition process (SPRP), which seeks to reduce the number of hypotheses using a minimal number of queries. We propose a number of policies for the SPRP which use maximum likelihood and information gain to choose which plan to query. We show this approach works well in practice on two domains from the literature, significantly reducing the number of hypotheses using fewer queries than a baseline approach. Our results can inform the design of future plan recognition systems that interleave the recognition process with intelligent interventions of their users.
UR - http://www.scopus.com/inward/record.url?scp=85006137687&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85006137687
SN - 1045-0823
VL - 2016-January
SP - 401
EP - 407
JO - IJCAI International Joint Conference on Artificial Intelligence
JF - IJCAI International Joint Conference on Artificial Intelligence
T2 - 25th International Joint Conference on Artificial Intelligence, IJCAI 2016
Y2 - 9 July 2016 through 15 July 2016
ER -