Reasoning for Autonomous Agents in Dynamic Domains
Reasoning for Autonomous Agents in Dynamic Domains
复制标题
动态域中自治代理的推理
DOI：
--
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
K. Geihs
中科院分区：
文献类型：
--
作者：
S. Opfer;Stefan Jakob;K. Geihs
与简单的自动吸尘器相比，多功能机器人可以端来一杯咖啡，
房间需要认知技能，如学习，计划和推理。尤其是动态推理
和人类居住的环境需要新的方法，可以处理全面和流畅的
知识基础一个很有前途的方法是答案集编程（ASP），提供多镜头解决
技术和非单调稳定模型语义。我们的目标是为多智能体系统配备
基于ASP的推理能力，使机器人团队能够科普动态环境。因此，我们认为，
我们结合了ALICA -一种交互式合作代理的语言-与ASP求解器Clingo，
选择拓扑路径规划作为我们的评估场景。我们使用区域连接演算作为
我们的评估的基本形式主义，并调查了我们的实现的可扩展性。结果
表明我们的方法可以处理动态环境，并扩展到适当的大问题大小。
In contrast to simple autonomous vacuum cleaners, multi-purpose robots that fetch a cup of coffee and clean
up rooms require cognitive skills such as learning, planning, and reasoning. Especially reasoning in dynamic
and human populated environments demands for novel approaches that can handle comprehensive and fluent
knowledge bases. A promising approach is Answer Set Programming (ASP), offering multi-shot solving
techniques and non-monotonic stable model semantics. Our objective is to equip multi-agent systems with
ASP-based reasoning capabilities, enabling a team of robots to cope with dynamic environments. Therefore,
we combined ALICA - A Language for Interactive Cooperative Agents - with the ASP solver Clingo and
chose topological path planning as our evaluation scenario. We utilised the Region Connection Calculus as
underlying formalism of our evaluation and investigated the scalability of our implementation. The results
show that our approach handles dynamic environments and scales up to appropriately large problem sizes.