General policies represent reactive strategies for solving large families of planning problems like the infinite collection of solvable instances from a given domain. Methods for learning such policies from a collection of small training instances have been developed successfully for classical domains. In this work, we extend the formulations and the resulting combinatorial methods for learning general policies over fully observable, non-deterministic (FOND) domains. We also evaluate the resulting approach experimentally over a number of benchmark domains in FOND planning, present the general policies that result in some of these domains, and prove their correctness. The method for learning general policies for FOND planning can actually be seen as an alternative FOND planning method that searches for solutions, not in the given state space but in an abstract space defined by features that must be learned as well.

We consider the problem of reaching a propositional goal condition in fully-observable nondeterministic (FOND) planning under a general class of fairness assumptions that are given explicitly. The fairness assumptions are of the form A/B and say that state trajectories that contain infinite occurrences of an action a from A in a state s and finite occurrence of actions from B, must also contain infinite occurrences of action a in s followed by each one of its possible outcomes. The infinite trajectories that violate this condition are deemed as unfair, and the solutions are policies for which all the fair trajectories reach a goal state. We show that strong and strong-cyclic FOND planning, as well as QNP planning, a planning model introduced recently for generalized planning, are all special cases of FOND planning with fairness assumptions of this form which can also be combined. FOND+ planning, as this form of planning is called, combines the syntax of FOND planning with some of the versatility of LTL for expressing fairness constraints. A sound and complete FOND+ planner is implemented by reducing FOND+ planning to answer set programs, and its performance is evaluated in comparison with FOND and QNP planners, and LTL synthesis tools. Two other FOND+ planners are introduced as well which are more scalable but are not complete.  

Existing FOND planning algorithms are effective and employ a wide range of techniques. However, most of the existing algorithms are not robust for dealing with both non-determinism and task size. In this paper, we develop a novel iterative depth-first search algorithm that solves FOND planning tasks and produces strong cyclic policies. Our algorithm is explicitly designed for FOND planning, addressing more directly the non-deterministic aspect of FOND planning, and it also exploits the benefits of heuristic functions to make the algorithm more effective during the iterative searching process.

Pruning techniques based on strong stubborn sets have recently shown their potential for SAS planning as heuristic search. Strong stubborn sets exploit operator independency to safely prune the search space. Like SAS planning, fully observable nondeterministic (FOND) planning faces the state explosion problem. However, it is unclear how stubborn set techniques carry over to the nondeterministics setting. In this paper, we introduce stubborn set pruning to FOND planning. We lift the notion of strong stubborn sets and introduce the conceptually more powerful notion of weak stubborn sets to FOND planning. Our experimental analysis shows that weak stubborn sets are beneficial to an LAO* search, and in particular show favorable performance when combined with symmetries and active operator pruning.

We consider the problem of reaching a propositional goal condition in fully-observable non-deterministic (FOND) planning under a general class of fairness assumptions that are given explicitly. The fairness assumptions are of the form A/B and say that state trajectories that contain infinite occurrences of an action a from A in a state s and finite occurrence of actions from B, must also contain infinite occurrences of action a in s followed by each one of its possible outcomes. The infinite trajectories that violate this condition are deemed as unfair, and the solutions are policies for which all the fair trajectories reach a goal state. We show that strong and strong-cyclic FOND planning, as well as QNP planning, a planning model introduced recently for generalized planning, are all special cases of FOND planning with fairness assumptions of this form which can also be combined. FOND+ planning, as this form of planning is called, combines the syntax of FOND planning with some of the versatility of LTL for expressing fairness constraints. A new planner is implemented by reducing FOND+ planning to answer set programs, and the performance of the planner is evaluated in comparison with FOND and QNP planners, and LTL synthesis tools.

In this report, we will define a new approach to the problem of non deterministic planning for extended temporal goals. In particular, we will give a solution to this problem reducing it to a fully observable non deterministic (FOND) planning problem and taking advantage of the LTLfToDFA tool. First of all, we will introduce the main idea and motivations supporting our approach. Then, we will give some preliminaries explaining the Planning Domain Definition Language (PDDL) language and the FOND planning problem formally. After that, we will illustrate our FOND4LTLfPLTLf (also available online) approach with the encoding of temporal goals into a PDDL domain and problem. Finally, we will present some of the results obtained through the application of the proposed solution.

Fully observable non-deterministic (FOND) planning is becoming increasingly important as an approach for computing proper policies in probabilistic planning, extended temporal plans in LTL planning, and general plans in generalized planning. In this work, we introduce a SAT encoding for FOND planning that is compact and can produce compact strong cyclic policies. Simple variations of the encodings are also introduced for strong planning and for what we call, dual FOND planning, where some non-deterministic actions are assumed to be fair (e.g., probabilistic) and others unfair (e.g., adversarial). The resulting FOND planners are compared empirically with existing planners over existing and new benchmarks. The notion of "probabilistic interesting problems" is also revisited to yield a more comprehensive picture of the strengths and limitations of current FOND planners and the proposed SAT approach.

We contribute to recent efforts in relating two approaches to automatic synthesis, namely, automated planning and discrete reactive synthesis. First, we develop a declarative characterization of the standard "fairness" assumption on environments in non-deterministic planning, and show that strong-cyclic plans are correct solution concepts for fair environments. This complements, and arguably completes, the existing foundational work on non-deterministic planning, which focuses on characterizing (and computing) plans enjoying special "structural" properties, namely loopy but closed policy structures. Second, we provide an encoding suitable for reactive synthesis that avoids the naive exponential state space blowup. To do so, special care has to be taken to specify the fairness assumption on the environment in a succinct manner.

Pruning techniques based on strong stubborn sets have recently shown their potential for SAS+ planning as heuristic search. Strong stubborn sets exploit operator independency to safely prune the search space. Like SAS+ planning, fully observable nondeterministic (FOND) planning faces the state explosion problem. However, it is unclear how stubborn set techniques carry over to the nondeterministics setting. In this paper, we introduce stubborn set pruning to FOND planning. We lift the notion of strong stubborn sets and introduce the conceptually more powerful notion of weak stubborn sets to FOND planning. Our experimental analysis shows that weak stubborn sets are beneficial to an LAO* search, and in particular show favorable performance when combined with symmetries and active operator pruning.

Whereas previous work on non-deterministic planning has focused on characterizing (and computing) "loopy" but "closed" plans, we look here at the kind of environments that these plans are to be executed in. In particular, we provide a logical characterization of the standard "fairness'' assumption used, and show that strong cyclic plans are correct solution concepts for fair environments.  We argue then that such logical characterization allows us to recast non-deterministic planning as a reactive synthesis task, and show that for a special case, recent efficient synthesis techniques can be applied.