Redesigning stochastic environments for maximized utility

Sarah Keren, Luis Pineda, Avigdor Gal, Erez Karpas, Shiomo Zilberstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We present the Utility Maximizing Design (UMD) model for optimally redesigning stochastic environments to achieve maximized performance. This model suits well contemporary applications that involve the design of environments where robots and humans co-exist an co-operate, e.g., vacuum cleaning robot. We discuss two special cases of the UMD model. The first is the equi-reward UMD (ER-UMD) in which the agents and the system share a utility function, such as for the vacuum cleaning robot. The second is the goal recognition design (GRD) setting, discussed in the literature, in which system and agent utilities are independent. To find the set of optimal modifications to apply to a UMD model, we present a generic method, based on heuristic search. After specifying the conditions for optimality in the general case, we present an admissible heuristic for the ER-UMD case. We also present a novel compilation that embeds the redesign process into a planning problem, allowing use of any off-the- shelf solver to find the best way to modify an environment when a design budget is specified. Our evaluation shows the feasibility of the approach using standard benchmarks from the probabilistic planning competition.

Original languageEnglish
Title of host publicationWS-17-01
Subtitle of host publicationArtificial Intelligence and Operations Research for Social Good; WS-17-02: Artificial Intelligence, Ethics, and Society; WS-17-03: Artificial Intelligence for Connected and Automated Vehicles; WS-17-04: Artificial Intelligence for Cyber Security; WS-17-05: Artificial Intelligence for Smart Grids and Buildings; WS-17-06: Computer Poker and Imperfect Information Games; WS-17-07: Crowdsourcing, Deep Learning and Artificial Intelligence Agents; WS-17-08: Distributed Machine Learning; WS-17-09: Joint Workshop on Health Intelligence; WS-17-10: Human-Aware Artificial Intelligence; WS-17-11: Human-Machine Collaborative Learning; WS-17-12: Knowledge-Based Techniques for Problem Solving and Reasoning; WS-17-13: Plan, Activity, and Intent Recognition; WS-17-14: Symbolic Inference and Optimization; WS-17-15: What's Next for AI in Games?
Number of pages9
ISBN (Electronic)9781577357865
StatePublished - 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: 4 Feb 20175 Feb 2017

Publication series

NameAAAI Workshop - Technical Report
VolumeWS-17-01 - WS-17-15


Conference31st AAAI Conference on Artificial Intelligence, AAAI 2017
Country/TerritoryUnited States
CitySan Francisco

All Science Journal Classification (ASJC) codes

  • Engineering(all)


Dive into the research topics of 'Redesigning stochastic environments for maximized utility'. Together they form a unique fingerprint.

Cite this