Designing agents that reason and act upon the world has always been one of the main objectives of the Artificial Intelligence community. While for planning in “simple” domains the agents can solely rely on facts about the world, in several contexts, e.g., economy, security, justice and politics, the mere knowledge of the world could be insufficient to reach a desired goal. In these scenarios, epistemic reasoning, i.e., reasoning about agents’ beliefs about themselves and about other agents’ beliefs, is essential to design winning strategies. This paper addresses the problem of reasoning in multi-agent epistemic settings exploiting declarative programming techniques. In particular, the paper presents an actual implementation of a multi-shot Answer Set Programming-based planner that can reason in multi-agent epistemic settings, called PLATO (ePistemic muLti-agent Answer seT programming sOlver). The ASP paradigm enables a concise and elegant design of the planner, w.r.t. other imperative implementations, facilitating the development of formal verification of correctness. 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.

中文翻译：

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在 ASP 中建模多智能体认知规划

设计对世界进行推理和行动的代理一直是人工智能社区的主要目标之一。虽然对于“简单”领域的规划，代理只能依赖有关世界的事实，但在几种情况下，例如，经济，安全，正义和政治，仅仅对世界的了解可能不足以达到预期的目标。在这些场景中，认知的推理，IE，推理代理人对自己的信念和其他代理人的信念，对于设计获胜策略至关重要。本文利用声明式编程技术解决了多智能体认知设置中的推理问题。特别是，本文介绍了多镜头的实际实现答案集编程- 可以在多智能体认知设置中推理的基于规划器，称为 PLATO（e磷局部亩大号代理一种回答吨编程○肝脏）。ASP 范式支持规划器的简洁和优雅的设计，以及其他命令式实现，促进正确性的形式验证的开发。该论文展示了规划器如何利用特定的认知状态表示和 ASP 求解器的效率，在从文献中收集的基准测试中获得具有竞争力的性能结果。