Risk Awareness in HTN Planning
This work addresses the need for risk-aware planning in domains such as autonomous vehicles, where existing HTN approaches assume risk neutrality, but it is incremental as it builds on existing HTN methods.
The paper tackles the problem of planning under uncertainty and risk in real-world domains like autonomous vehicles, where action costs are uncertain, by enhancing Hierarchical Task Network (HTN) planning with risk awareness using expected utility theory, resulting in a framework that allows agents to compute plans tailored to specific risk attitudes, as demonstrated in empirical case studies.
Actual real-world domains are characterised by uncertain situations in which acting and using resources may entail the embracing of risks. Performing actions in such domains involves costs of consuming some resource, such as time or energy, where the knowledge about these costs can range from known to totally unknown. In autonomous vehicles, actions have uncertain costs due to factors like traffic. Choosing an action requires assessing delay risks, as each road may have unpredictable congestion. Thus, these domains call for not only planning under uncertainty but also planning while embracing risk. Resorting to HTN planning as a widely used planning technique in real-world applications, one can observe that existing approaches assume risk neutrality, relying on single-valued action costs without considering risk. Here, we enhance HTN planning with risk awareness by considering expected utility theory. We introduce a general framework for HTN planning that allows modelling risk and uncertainty using a probability distribution of action costs upon which we define risk-aware HTN planning as being capable of accounting for the different risk attitudes and allowing the computation of plans that go beyond risk neutrality. We lay out that computing risk-aware plans requires finding plans with the highest expected utility. We argue that it is possible for HTN planning agents to solve specialised risk-aware HTN planning problems by adapting existing HTN planning approaches, and develop an approach that surpasses the expressiveness of current approaches by allowing these agents to compute plans tailored to a particular risk attitude. An empirical evaluation of two case studies highlights the feasibility and expressiveness of this approach. We also highlight open issues, such as applying the proposal beyond HTN planning, covering both modelling and plan generation.