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Actual real-world domains are characterised by uncertain situations in whichacting and use of resources require embracing risk. Performing actions in suchdomains always entails costs of consuming some resource, such as time, money,or energy, where the knowledge about these costs can range from totally knownto totally unknown and even unknowable probabilities of costs. Think of roboticdomains, where actions and their costs are non-deterministic due to uncertainfactors like obstacles. Choosing which action to perform considering its coston the available resource requires taking a stance on risk. Thus, these domainscall for not only planning under uncertainty but also planning while embracingrisk. Taking Hierarchical Task Network (HTN) planning as a widely used planningtechnique in real-world applications, one can observe that existing approachesdo not account for risk. That is, computing most probable or optimal plansusing actions with single-valued costs is only enough to express riskneutrality. In this work, we postulate that HTN planning can become risk awareby considering expected utility theory, a representative concept of decisiontheory that enables choosing actions considering a probability distribution oftheir costs and a given risk attitude expressed using a utility function. Inparticular, we introduce a general framework for HTN planning that allowsmodelling risk and uncertainty using a probability distribution of action costsupon which we define risk-aware HTN planning as an approach that accounts forthe different risk attitudes and allows computing plans that go beyond riskneutrality. In fact, we layout that computing risk-aware plans requires findingplans with the highest expected utility. Finally, we argue that it is possiblefor HTN planning agents to solve specialised risk-aware HTN planning problemsby adapting some existing HTN planning approaches.

Risk Awareness in HTN Planning