Planning Over Multi-Agent Epistemic States: A Classical Planning Approach
By Christian Muise, Vaishak Belle, Paolo Felli, Sheila A. McIlraith, Tim Miller, Adrian R. Pearce, Liz Sonenberg. 2015
122 citations recorded by the source corpus.
Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Stefano V. Albrecht
- Peter Stone
- Vaishak Belle
- Tim Miller
- Filippos Christianos
- Matthew E. Taylor
- Sergey Levine
- Georgios Papoudakis
Citations in the corpus
- Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds (2017, background): “There is a large body of work on the model theory and implementation of meta-beliefs with multiple agents [Fagin et al. , 1995; Muise et al. , 2015], but in the context of the situation calculus, reduction theorems and axiomatizations have been studied in [Kelly and Pearce, 2008; Liu and Wen, 2011;…”
- Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “Halpern and Pearl [59] already considers a simplified idea of this in their model of explanation, but other work on epistemic reasoning and planning [42, 135] and planning for interactive dialogue [142] can play a part here.”
- Autonomous agents modelling other agents: A comprehensive survey and open problems (2017, methodology): “Muise et al. (2015) and Kominis and Ge ff ner (2015) both propose methods that solve epistemic planning problems using classical planning algorithms.”
- Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning (2019, unknown): “Since then, alternative approaches, including logics-based models (Bolander & Andersen, 2011; Muise et al., 2015) or graphical models (Doshi et al., 2009; Gal & Pfeffer, 2003; 2008), have been adopted.”