Reasoning for Autonomous Agents in Dynamic Domains: Towards Automatic Satisfaction of the Module Property.

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.