Human Goal Recognition as Bayesian Inference: Investigating the Impact of Actions, Timing, and Goal Solvability
Pages 2066 - 2074
Abstract
Goal recognition is a fundamental cognitive process that enables individuals to infer intentions based on available cues. Current goal recognition algorithms often take only observed actions as input, but here we use a Bayesian framework to explore the role of actions, timing, and goal solvability in goal recognition. We analyze human responses to goal-recognition problems in the Sokoban domain, and find that actions are assigned most importance, but that timing and solvability also influence goal recognition in some cases, especially when actions are uninformative. We leverage these findings to develop a goal recognition model that matches human inferences more closely than do existing algorithms. Our work provides new insight into human goal recognition and takes a step towards more human-like AI models.
References
[1]
Abeer Alshehri, Tim Miller, and Mor Vered. 2023. Explainable Goal Recognition: A Framework Based on Weight of Evidence. Proceedings of the International Conference on Automated Planning and Scheduling, Vol. 33, 1 (Jul. 2023), 7--16. https://doi.org/10.1609/icaps.v33i1.27173
[2]
Chris L Baker, Julian Jara-Ettinger, Rebecca Saxe, and Joshua B Tenenbaum. 2017. Rational quantitative attribution of beliefs, desires and percepts in human mentalizing. Nature Human Behaviour, Vol. 1, 4 (2017), 0064.
[3]
Chris L Baker, Rebecca Saxe, and Joshua B Tenenbaum. 2009. Action understanding as inverse planning. Cognition, Vol. 113, 3 (2009), 329--349.
[4]
Marlene Berke, Abigail Tenenbaum, Ben Sterling, and Julian Jara-Ettinger. 2023. Thinking about Thinking as Rational Computation. (2023).
[5]
Donald O Case and Lisa M Given. 2016. Looking for information: A survey of research on information seeking, needs, and behavior. (2016).
[6]
Eugene Charniak and Robert P Goldman. 1993. A Bayesian model of plan recognition. Artificial Intelligence, Vol. 64, 1 (1993), 53--79.
[7]
Vael Gates, Frederick Callaway, Mark K Ho, and Thomas L Griffiths. 2021. A rational model of people's inferences about others' preferences based on response times. Cognition, Vol. 217 (2021), 104885.
[8]
Kin Max Gusmao, Ramon Fraga Pereira, and Felipe Meneguzzi. 2021. Inferring Agents Preferences as Priors for Probabilistic Goal Recognition. arXiv preprint arXiv:2102.11791 (2021).
[9]
Spencer E Harpe. 2015. How to analyze Likert and other rating scale data. Currents in pharmacy teaching and learning, Vol. 7, 6 (2015), 836--850.
[10]
Sarah Keren, Avigdor Gal, and Erez Karpas. 2015. Goal recognition design for non-optimal agents. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 29.
[11]
Aleck M MacNally, Nir Lipovetzky, Miquel Ramirez, and Adrian R Pearce. 2018. Action selection for transparent planning. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. 1327--1335.
[12]
Peta Masters, Michael Kirley, and Wally Smith. 2021. Extended goal recognition: A planning-based model for strategic deception. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems. 871--879.
[13]
Peta Masters and Sebastian Sardina. 2019. Goal recognition for rational and irrational agents. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. 440--448.
[14]
Laura R Novick and Steven J Sherman. 2003. On the nature of insight solutions: Evidence from skill differences in anagram solution. The Quarterly Journal of Experimental Psychology Section A, Vol. 56, 2 (2003), 351--382.
[15]
Ramon Pereira, Nir Oren, and Felipe Meneguzzi. 2017. Landmark-based heuristics for goal recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31.
[16]
Miquel Ramírez and Hector Geffner. 2009. Plan recognition as planning. In Twenty-First International Joint Conference on Artificial Intelligence.
[17]
Miguel Ramírez and Hector Geffner. 2010. Probabilistic plan recognition using off-the-shelf classical planners. In Twenty-Fourth AAAI Conference on Artificial Intelligence.
[18]
Ronal Singh, Tim Miller, Joshua Newn, Liz Sonenberg, Eduardo Velloso, and Frank Vetere. 2018. Combining planning with gaze for online human intention recognition. In Proceedings of the 17th international conference on autonomous agents and multiagent systems. 488--496.
[19]
Shirin Sohrabi, Anton V Riabov, and Octavian Udrea. 2016. Plan Recognition as Planning Revisited. In IJCAI. 3258--3264.
[20]
Sascha Topolinski, Giti Bakhtiari, and Thorsten M Erle. 2016. Can I cut the Gordian tnok? The impact of pronounceability, actual solvability, and length on intuitive problem assessments of anagrams. Cognition, Vol. 146 (2016), 439--452.
[21]
Mor Vered, Gal A Kaminka, and Sivan Biham. 2016. Online goal recognition through mirroring: Humans and agents. In The Fourth Annual Conference on Advances in Cognitive Systems.
[22]
Chenyuan Zhang, Charles Kemp, and Nir Lipovetzky. 2023 a. Goal Recognition with Timing Information. Proceedings of the International Conference on Automated Planning and Scheduling, Vol. 33, 1 (Jul. 2023), 443--451. https://doi.org/10.1609/icaps.v33i1.27224
[23]
Chenyuan Zhang, Nir Lipovetzky, and Charles Kemp. 2023 b. Comparing AI planning algorithms with humans on the Tower of London task. In ICAPS 2023 Workshop on Human-Aware Explainable Planning.
[24]
Tan Zhi-Xuan, Jordyn L Mann, Tom Silver, Joshua B Tenenbaum, and Vikash K Mansinghka. 2020. Online Bayesian goal inference for boundedly-rational planning agents. arXiv preprint arXiv:2006.07532 (2020).
Index Terms
- Human Goal Recognition as Bayesian Inference: Investigating the Impact of Actions, Timing, and Goal Solvability
Recommendations
Implementation of a GOAL PROGRAM
SIGUCCS '75: Proceedings of the 3rd annual ACM SIGUCCS conference on User servicesWe all need specific goals to guide us. Goals give you direction. Recognizing that we need goals is the first step toward choosing and achieving those goals.
By making each goal specific, you have definite deadlines to aim for. Get a statement of your ...
Towards Online Goal Recognition Combining Goal Mirroring and Landmarks
AAMAS '18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent SystemsTemporally extended goal recognition in fully observable non-deterministic domain models: Temporally extended goal recognition in FOND planning
AbstractGoal Recognition is the task of discerning the intended goal that an agent aims to achieve, given a set of goal hypotheses, a domain model, and a sequence of observations (i.e., a sample of the plan executed in the environment). Existing ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2024
2898 pages
ISBN:9798400704864
- General Chairs:
- Mehdi Dastani,
- Jaime Simão Sichman,
- Program Chairs:
- Natasha Alechina,
- Virginia Dignum
Sponsors
Publisher
International Foundation for Autonomous Agents and Multiagent Systems
Richland, SC
Publication History
Published: 06 May 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- Australian Research Council
Conference
AAMAS '24
Sponsor:
AAMAS '24: International Conference on Autonomous Agents and Multiagent Systems
May 6 - 10, 2024
Auckland, New Zealand
Acceptance Rates
Overall Acceptance Rate 1,155 of 5,036 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 16Total Downloads
- Downloads (Last 12 months)16
- Downloads (Last 6 weeks)1
Reflects downloads up to 09 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in