Reasoning for Autonomous Agents in Dynamic Domains

Abstract

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.