In many usage scenarios of AI Planning technology, users will want not just a plan π but an explanation of the space of possible plans, justifying π. In particular, in oversubscription planning where not all goals can be achieved, users may ask why a conjunction A of goals is not achieved by π. We propose to answer this kind of question with the goal conjunctions B excluded by A, i. e., that could not be achieved if A were to be enforced. We formalize this approach in terms of plan-property dependencies, where plan properties are propositional formulas over the goals achieved by a plan, and dependencies are entailment relations in plan space. We focus on entailment relations of the form ∧g∈A g ⇒ ⌝ ∧g∈B g, and devise analysis techniques globally identifying all such relations, or locally identifying the implications of a single given plan property (user question) ∧g∈A g. We show how, via compilation, one can analyze dependencies between a richer form of plan properties, specifying formulas over action subsets touched by the plan. We run comprehensive experiments on adapted IPC benchmarks, and find that the suggested analyses are reasonably feasible at the global level, and become significantly more effective at the local level.
In classical planning as heuristic search, the guiding heuristic function is typically treated as a black box. While many heuristics support refinement operations, they are typically only used for its initialization before search, but further refinement during search could make use of additional information not available in the initial state. We explore online refinement for additive Cartesian abstraction heuristics. These abstractions are computed through counter-example guided abstraction refinement, which can be applied online as well to further improve the abstractions. We introduce three operations, refinement, merging, and reordering, which are combined to a converging online-refinement algorithm. We describe how online refinement can effectively be used in A* and evaluate our approach on the IPC benchmarks, where it outperforms offline-generated abstractions in many domains.
Recent work suggests to explain trade-offs between soft-goals in terms of their conflicts, i.e., minimal unsolvable soft-goal subsets. But this does not explain the conflicts themselves: Why can a given set of soft-goals not be jointly achieved? Here we approach that question in terms of the underlying constraints on plans in the task at hand, namely resource availability and time windows. In this context, a natural form of explanation for a soft-goal conflict is a minimal constraint relaxation under which the conflict disappears (``if the deadline was 1 hour later, it would work''). We explore algorithms for computing such explanations. A baseline is to simply loop over all relaxed tasks and compute the conflicts for each separately. We improve over this by two algorithms that leverage information -- conflicts, reachable states -- across relaxed tasks. We show that these algorithms can exponentially outperform the baseline in theory, and we run experiments confirming that advantage in practice.
The trade-offs between different desirable plan properties -- e.g. PDDL temporal plan preferences -- are often difficult to understand. Recent work addresses this by iterative planning with explanations elucidating the dependencies between such plan properties. Users can ask questions of the form ``Why does the plan not satisfy property p?'', which are answered by ``Because then we would have to forego q''. It has been shown that such dependencies can be computed reasonably efficiently. But is this form of explanation actually useful for users? We run a large crowd-worker user study (N=100 in each of 3 domains) evaluating that question. To enable such a study in the first place, we contribute a Web-based platform for iterative planning with explanations, running in standard browsers. Comparing users with vs. without access to the explanations, we find that the explanations enable users to identify better trade-offs between the plan properties, indicating an improved understanding of the planning task.
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