{"type":"ScholarlyArticle","title":"New Metrics and Algorithms for Stochastic Goal Recognition Design Problems","authors":[{"name":"Christabel Wayllace","slug":"christabel-wayllace","given":"Christabel","family":"Wayllace","affiliation":"Computer Science Department New Mexico State University Las Cruces , NM 88003 , USA"},{"name":"Ping Hou","slug":"ping-hou","given":"Ping","family":"Hou","affiliation":"Computer Science Department New Mexico State University Las Cruces , NM 88003 , USA"},{"name":"William Yeoh","slug":"william-yeoh","given":"William","family":"Yeoh","affiliation":"Computer Science Department New Mexico State University Las Cruces , NM 88003 , USA"}],"abstract":"Goal Recognition Design (GRD) problems involve identifying the best ways to modify the underlying environment that agents operate in, typically by making a subset of feasible actions infeasible, in such a way that agents are forced to reveal their goals as early as possible. The Stochastic GRD (S-GRD) model is an important extension that introduced stochasticity to the outcome of agent actions. Unfortunately, the worst-case distinctiveness (wcd) metric proposed for S-GRDs has a formal definition that is inconsistent with its intuitive definition, which is the maximal number of actions an agent can take, in the expectation, before its goal is revealed. In this paper, we make the following contributions: (1) We propose a new wcd metric, called all-goals wcd (wcdag), that remedies this inconsistency; (2) We introduce a new metric, called expected-case distinctiveness (ecd), that weighs the possible goals based on their importance; (3) We provide theoretical results comparing these different metrics as well as the complexity of computing them optimally; and (4) We describe new efficient algorithms to compute the wcdag and ecd values.","doi":"10.24963/ijcai.2017/622","url":"https://ijcai.org/proceedings/2017/622","pdf":"https://ijcai.org/proceedings/2017/0622.pdf","markdown":"https://ijcai.org/proceedings/2017/622.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/622","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Planning and Scheduling","track":"Main track","number":622,"pages":{"first":4455,"last":4462},"keywords":["Planning and Scheduling: Markov Decisions Processes","Planning and Scheduling: Activity and Plan Recognition"],"videos":[],"references":[{"position":1,"raw":"[Bellman, 1957] Richard Bellman. 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