Reasoning for Autonomous Agents in Dynamic Domains: Towards Automatic Satisfaction of the Module Property
Reasoning for Autonomous Agents in Dynamic Domains: Towards Automatic Satisfaction of the Module Property
复制标题
动态域中自治代理的推理：走向模块属性的自动满足
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
K. Geihs
中科院分区：
文献类型：
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作者：
S. Opfer;Stefan Jakob;K. Geihs
最先进的服务机器人，拿一杯咖啡和打扫房间，需要认知技能，如学习，规划和推理。特别是在动态和人类居住的环境中的推理需要新的方法，可以处理全面和流畅的知识库。我们的长期目标是建立一个能够处理动态和家庭环境的自主机器人团队。因此，我们结合了ALICA -一种用于交互式合作代理的语言-与答案集编程求解器Clingo。答案集编程方法提供了多点求解技术和非单调稳定模型语义，但要求满足模块性质。我们开发了一个自动满足的模块属性，并选择拓扑路径规划作为我们的评估方案。我们利用区域连接演算作为我们评估的基本形式，并研究了我们实现的可扩展性。结果表明，我们的方法处理动态环境和规模适当的大问题的大小，同时自动满足模块属性。
State-of-the-art service 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. Our long-term objective is an autonomous robotic team that is capable of handling dynamic and domestic environments. Therefore, we combined ALICA – A Language for Interactive Cooperative Agents – with the Answer Set Programming solver Clingo. The answer set programming approach offers multi-shot solving techniques and non-monotonic stable model semantics, but requires to keep the Module Property satisfied. We developed an automatic satisfaction of the Module Property and chose topological path planning as our evaluation scenario. We utilised the Region Connection Calculus as the 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 while automatically satisfying the Module Property.