ECHO: A hierarchical combination of classical and multi-agent epistemic planning problems

Author:

Soldà Davide1,Fabiano Francesco2,Dovier Agostino3

Affiliation:

1. Institute of Logic and Computation, Technische Universität Wien , Favoritenstraße 9-11, A-1040 Wien, Austria

2. Department of Mathematical Physical and Computer Sciences, University of Parma , Parco Area delle Scienze 53/A, I-43124 Parma, Italy

3. Department of Mathematics, Computer Science and Physics, University of Udine , Via delle Scienze 206, I-33100 Udine, Italy

Abstract

Abstract The continuous interest in Artificial Intelligence (AI) has brought, among other things, the development of several scenarios where multiple artificial entities interact with each other. As for all the other autonomous settings, these multi-agent systems require orchestration. This is, generally, achieved through techniques derived from the vast field of Automated Planning. Notably, arbitration in multi-agent domains is not only tasked with regulating how the agents act, but must also consider the interactions between the agents’ information flows and must, therefore, reason on an epistemic level. This brings a substantial overhead that often diminishes the reasoning process’s usability in real-world situations. To address this problem, we present ECHO, a hierarchical framework that embeds classical and multi-agent epistemic (epistemic, for brevity) planners in a single architecture. The idea is to combine (i) classical; and(ii) epistemic solvers to model efficiently the agents’ interactions with the (i) ‘physical world’; and(ii) information flows, respectively. In particular, the presented architecture starts by planning on the ‘epistemic level’, with a high level of abstraction, focusing only on the information flows. Then it refines the planning process, due to the classical planner, to fully characterize the interactions with the ‘physical’ world. To further optimize the solving process, we introduced the concept of macros in epistemic planning and enriched the ‘classical’ part of the domain with goal-networks. Finally, we evaluated our approach in an actual robotic environment showing that our architecture indeed reduces the overall computational time.

Publisher

Oxford University Press (OUP)

Subject

Logic,Hardware and Architecture,Arts and Humanities (miscellaneous),Software,Theoretical Computer Science

Reference51 articles.

1. Epistemic planning (Dagstuhl seminar 17231);Baral;Dagstuhl Reports,2017

2. An action language for multi-agent domains;Baral;Artificial Intelligence,2022

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