Causality, Responsibility and Blame in Team Plans
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Abstract (arXiv)
Many objectives can be achieved (or may be achieved more effectively) only by a group of agents executing a team plan. If a team plan fails, it is often of interest to determine what caused the failure, the degree of responsibility of each agent for the failure, and the degree of blame attached to each agent. We show how team plans can be represented in terms of structural equations, and then apply the definitions of causality introduced by Halpern [2015] and degree of responsibility and blame introduced by Chockler and Halpern [2004] to determine the agent(s) who caused the failure and what their degree of responsibility/blame is. We also prove new results on the complexity of computing causality and degree of responsibility and blame, showing that they can be determined in polynomial time for many team plans of interest.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2020.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 5.
9 papers.
- 2022Actual Causality and Responsibility Attribution in Decentralized Partially Observable Markov Decision Processescited by 8
- 2023Anticipating Responsibility in Multiagent Planningcited by 4
- 2023Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPscited by 3
- 2024Automated Discovery of Functional Actual Causes in Complex Environmentscited by 3
- 2022Moral reinforcement learning using actual causationcited by 1
- 2024Responsibility-aware Strategic Reasoning in Probabilistic Multi-Agent Systemscited by 1
- 2024Measuring Responsibility in Multi-Agent Systemscited by 1
- 2024The Benefits of Power Regularization in Cooperative Reinforcement Learningcited by 0
- 2024Responsibility in a Multi-Value Strategic Settingcited by 0
- 2024Measuring Responsibility in Multi-Agent Systems31 Oct 2024
- 2024Responsibility-aware Strategic Reasoning in Probabilistic Multi-Agent Systems31 Oct 2024
- 2024Responsibility in a Multi-Value Strategic Setting22 Oct 2024
- 2024The Benefits of Power Regularization in Cooperative Reinforcement Learning17 Jun 2024
- 2024Automated Discovery of Functional Actual Causes in Complex Environments16 Apr 2024
- 2023Anticipating Responsibility in Multiagent Planning31 Jul 2023
- 2023Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPs24 Feb 2023
- 2022Moral reinforcement learning using actual causation17 May 2022
- 2022Actual Causality and Responsibility Attribution in Decentralized Partially Observable Markov Decision Processes1 Apr 2022