Abstract: In the context of deliberative reasoning, autonomous systems should be able to explicitly reason about goals. Most research in automated planning assume that goals are initially given and fixed and planners generate plans to achieve them. However, in some real-world scenarios where planners work in an on-line continual planning setting, additional goals may arrive over time. When information about future goal arrivals is known, it can be exploited to direct the system towards those goals even though they have not arrived yet. Recent work presented an approach that exhibited such anticipatory behavior based on hindsight optimization on a domain-dependent setup. In this paper, we tackle this problem from the point of view of domain-independent planning. The available per-step time in on-line continual problems can be very short and domain-independent planners can scale poorly in such short time. In fact, a domain-independent reimplementation of the hindsight optimization scheme may not even be applicable. We propose several alternative approaches that consider future goals in a domain-independent planning process. Experimental results in several benchmark domains suggest that these approaches exhibit a more efficient and effective behavior than a reactive approach in which goals are pursued after their arrival.
Keywords: Goal reasoning, Automated Planning, AP, Artificial Intelligence, AI
