15 accepted papers
Neural networks are increasingly important to learn action policies. Policy predicate abstraction (PPA) verifies safety of such a neural policy pi by over-approximating the state space subgraph induced by pi and using counterexample-guided abstraction refinement (CEGAR) to iteratively refine the abs
We propose a new approach based on ranking to learn to guide Greedy Best-First Search (GBFS). As previous ranking approaches, ours is based on the observation that directly learning a heuristic function is overly restrictive, and that GBFS is capable of efficiently finding good plans for a much more…
Machine-learning models are becoming increasingly prevalent in our lives, for instance assisting in image-classification or decision-making tasks. Consequently, the reliability of these models is of critical importance and has resulted in the development of numerous approaches for validating and ver…
It is often natural in planning to specify conditions that should be avoided, characterizing dangerous or highly undesirable behavior. PDDL3 supports this with temporal-logic state trajectory constraints. Here we focus on the simpler case where the constraint is a non-temporal formula ? - the avoid…
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 underly…
State constraints in AI Planning globally restrict the legal environment states. Standard planning languages make closed-domain and closed-world assumptions. Here we address open-world state constraints formalized by planning over a description logic (DL) ontology. Previously, this combination of DL…
While state-of-the-art planning systems need a grounded (propositional) task representation, the input model is provided "lifted", specifying predicates and action schemas with variables over a finite object universe. The size of the grounded model is exponential in predicate/action-schema arity, li…
Symbolic search, using Binary Decision Diagrams (BDDs) to represent sets of states, is a competitive approach to optimal planning. Yet heuristic search in this context remains challenging. The many advances on admissible planning heuristics are not directly applicable, as they evaluate one state at…
The need to replan arises in many applications. However, in the context of planning as heuristic search, it raises an annoying problem: if the previous plan is still executing, what should the new plan search take as its initial state? If it were possible to accurately predict how long replanning wo…
Classical planning tasks are commonly described in PDDL, while most planning systems operate on a grounded finite-domain representation (FDR). The translation of PDDL into FDR is complex and has a lot of choice points---it involves identifying so called mutex groups---but most systems rely on the tr…
Stackelberg planning is a recent framework where a leader and a follower each choose a plan in the same planning task, the leader's objective being to maximize plan cost for the follower. This formulation naturally captures security-related (leader=defender, follower=attacker) as well as robustness-…
Temporal plan preferences are natural and important in a variety of applications. Yet users often find it difficult to formalize their preferences. Here we explore the possibility to learn preferences from example plans. Focusing on one preference at a time, the user is asked to annotate examples as…
Polynomial-time heuristic functions for planning are commonplace since 20 years. But polynomial-time in which input? Almost all existing approaches are based on a grounded task representation, not on the actual PDDL input which is exponentially smaller. This limits practical applicability to cases w…
When generating technical instructions, it is often convenient to describe complex objects in the world at different levels of abstraction. A novice user might need an object explained piece by piece, while for an expert, talking about the complex object (e.g. a wall or railing) directly may be more…
Justifying a plan to a user requires answering questions about the space of possible plans. Recent work introduced a framework for doing so via plan-property dependencies, where plan properties p are Boolean functions on plans, and p entails q if all plans that satisfy p also satisfy q. We extend th…