Comprehensive Multi-Agent Epistemic Planning

• URL: http://arxiv.org/abs/2109.08301v1
• Date: Fri, 17 Sep 2021 01:50:18 GMT
• Title: Comprehensive Multi-Agent Epistemic Planning
• Authors: Francesco Fabiano (University of Udine)
• Abstract summary: This manuscript is focused on a specialized kind of planning known as Multi-agent Epistemic Planning (MEP). EP refers to an automated planning setting where the agent reasons in the space of knowledge/beliefs states and tries to find a plan to reach a desirable state from a starting one. Its general form, the MEP problem, involves multiple agents who need to reason about both the state of the world and the information flows between agents.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the last few years, the concept of Artificial Intelligence has become central in different tasks concerning both our daily life and several working scenarios. Among these tasks automated planning has always been central in the AI research community. In particular, this manuscript is focused on a specialized kind of planning known as Multi-agent Epistemic Planning (MEP). Epistemic Planning (EP) refers to an automated planning setting where the agent reasons in the space of knowledge/beliefs states and tries to find a plan to reach a desirable state from a starting one. Its general form, the MEP problem, involves multiple agents who need to reason about both the state of the world and the information flows between agents. To tackle the MEP problem several tools have been developed and, while the diversity of approaches has led to a deeper understanding of the problem space, each proposed tool lacks some abilities and does not allow for a comprehensive investigation of the information flows. That is why, the objective of our work is to formalize an environment where a complete characterization of the agents' knowledge/beliefs interaction and update is possible. In particular, we aim to achieve such goal by defining a new action-based language for multi-agent epistemic planning and to implement an epistemic planner based on it. This solver should provide a tool flexible enough to reason on different domains, e.g., economy, security, justice and politics, where considering others' knowledge/beliefs could lead to winning strategies.

Related papers

• Revealing the Barriers of Language Agents in Planning [44.913745512049246]
  We show that current language agents still lack human-level planning abilities. Even the state-of-the-art reasoning model, OpenAI o1, achieves only 15.6% on one of the complex real-world planning benchmarks. We identify two key factors that hinder agent planning: the limited role of constraints and the diminishing influence of questions.
  arXiv  Detail & Related papers  (2024-10-16T09:44:38Z)
• Ask-before-Plan: Proactive Language Agents for Real-World Planning [68.08024918064503]
  Proactive Agent Planning requires language agents to predict clarification needs based on user-agent conversation and agent-environment interaction. We propose a novel multi-agent framework, Clarification-Execution-Planning (textttCEP), which consists of three agents specialized in clarification, execution, and planning.
  arXiv  Detail & Related papers  (2024-06-18T14:07:28Z)
• TravelPlanner: A Benchmark for Real-World Planning with Language Agents [63.199454024966506]
  We propose TravelPlanner, a new planning benchmark that focuses on travel planning, a common real-world planning scenario. It provides a rich sandbox environment, various tools for accessing nearly four million data records, and 1,225 meticulously curated planning intents and reference plans. Comprehensive evaluations show that the current language agents are not yet capable of handling such complex planning tasks-even GPT-4 only achieves a success rate of 0.6%.
  arXiv  Detail & Related papers  (2024-02-02T18:39:51Z)
• Automated Process Planning Based on a Semantic Capability Model and SMT [50.76251195257306]
  In research of manufacturing systems and autonomous robots, the term capability is used for a machine-interpretable specification of a system function. We present an approach that combines these two topics: starting from a semantic capability model, an AI planning problem is automatically generated.
  arXiv  Detail & Related papers  (2023-12-14T10:37:34Z)
• Learning adaptive planning representations with natural language guidance [90.24449752926866]
  This paper describes Ada, a framework for automatically constructing task-specific planning representations. Ada interactively learns a library of planner-compatible high-level action abstractions and low-level controllers adapted to a particular domain of planning tasks.
  arXiv  Detail & Related papers  (2023-12-13T23:35:31Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)
• Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief [27.524600740450126]
  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. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.
  arXiv  Detail & Related papers  (2021-10-06T03:24:01Z)
• E-PDDL: A Standardized Way of Defining Epistemic Planning Problems [11.381221864778976]
  Epistemic Planning (EP) refers to an automated planning setting where the agent reasons in the space of knowledge states. We propose a unified way of specifying EP problems - the Epistemic Planning Domain Language, EPDDL. We show that E-PDDL can be supported by leading MEP planners and provide corresponding code that translates MEP problems into (MEP) problems that can be handled by several planners.
  arXiv  Detail & Related papers  (2021-07-19T10:20:20Z)
• Modelling Multi-Agent Epistemic Planning in ASP [66.76082318001976]
  This paper presents an implementation of a multi-shot Answer Set Programming-based planner that can reason in multi-agent epistemic settings. The paper shows how the planner, exploiting an ad-hoc epistemic state representation and the efficiency of ASP solvers, has competitive performance results on benchmarks collected from the literature.
  arXiv  Detail & Related papers  (2020-08-07T06:35:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.