University of Kassel, Germany
Answer Set Programming, Region Connection Calculus, Spatial Reasoning, Multi-shot Solving.
Knowledge Representation and Reasoning
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 sc
ales up to appropriately large problem sizes.