On representing planning domains under uncertainty

TL;DRAbstract

Abstract—Planning is an important activity in military coali-tions and the support of an automated planning tool could help military planners by reducing the cognitive burden of their work. Current AI planning paradigms use two different types of formalism to represent the planning problem. Each of these formalisms entails different inference algorithms and representation of results. On the one hand plans in non-stochastic domains are rep-resented using declarative logic-based formalisms, an example of which is Hierarchical Task Networks (HTNs). In HTNs, domains are represented in terms of task decompositions of increased detail in relation to the actions that must be carried out. In general, declarative formalisms are easier for humans to understand. On the other hand, stochastic planning is often rep-resented in terms of large probability functions that exhaustively specify the likelihood of relevant world changes when actions are executed, as exemplified by Markov Decision Processes

Chat with Paper

AI Agents for this Paper