We propose a compilation that enhances a given classical planning task to compute plans that contain control flow and procedure calls. Control flow instructions and procedures allow us to generate compact and general solutions able to solve planning tasks for which multiple unit tests are defined. The paper analyzes the relation between classical planning and structured programming with unit tests and shows how to exploit this relation in a classical planning compilation. In experiments, we evaluate the empirical performance of the compilation using an off-the-shelf classical planner and show that we can compress classical planning solutions and that these compressed solutions can solve planning tasks with multiple tests.

Restricting the search space has shown to be an effective approach for improving the performance of automated planning systems. A planner-independent technique for pruning the search space is domain and problem reformulation. Recently, Outer Entanglements, which are relations between planning operators and initial or goal predicates, have been introduced as a reformulation technique for eliminating potential undesirable instances of planning operators, and thus restricting the search space. Reformulation techniques, however, have been mainly applied in classical planning, although many real-world planning applications require to deal with numerical information. In this paper, we investigate the usefulness of reformulation approaches in planning with numerical fluents. In particular, we propose and extension of the notion of outer entanglements for handling numeric fluents. An empirical evaluation, which involves 150 instances from 5 domains, shows promising results.

Hierarchical Task Network (HTN) planning with task insertion (TIHTN planning) is a variant of HTN planning. In HTN planning, the only means to alter task networks is to decompose compound tasks. In TIHTN planning, tasks may also be inserted directly. In this paper we provide tight complexity bounds for TIHTN planning along two axis: whether variables are allowed and whether methods must be totally ordered.

This paper describes a system that automatically transforms a PDDL encoding, calls a planner to solve the transformed representation, and translates the solution back into the original representation. The approach involves counting objects that are indistinguishable, rather than treating them as individuals, which eliminates some unnecessary combinatorial explosion.

In the last decade the emphasis on improving the operational performance of domain independent automated planners has been in developing complex techniques which merge a range of different strategies. This quest for operational advantage, driven by the regular international planning competitions, has not made it easy to study, understand and predict what combinations of techniques will have what effect on a planner’s behaviour in a particular application domain. In this paper, we consider two machine learning techniques for planner performance improvement, and exploit a modular approach to their combination in order to facilitate the analysis of the impact of each individual component. We believe this can contribute to the development of more transparent planning engines, which are designed using modular, interchangeable, and well-founded components. Specifically, we combined two previously unrelated learning techniques, entanglements and relational decision trees, to guide a “vanilla” search algorithm. We report on a large experimental analysis which demonstrates the effectiveness of the approach in terms of performance improvements, resulting in a very competitive planning configuration despite the use of a more modular and transparent architecture. This gives insights on the strengths and weaknesses of the considered approaches, that will help their future exploitation.

Red-black planning is a recent approach to partial delete relaxation, where red variables take the relaxed semantics (accumulating their values), while black variables take the regular semantics. Practical heuristic functions can be generated from tractable sub-classes of red-black planning. Prior work has identified such sub-classes based on the black causal graph, i.e., the projection of the causal graph onto the black variables. Here, we consider cross-dependencies between black and red variables instead. We show that, if no red variable relies on black preconditions, then red-black plan generation is tractable in the size of the black state space, i.e., the product of the black variables. We employ this insight to devise a new red-black plan heuristic in which variables are painted black starting from the causal graph leaves. We evaluate this heuristic on the planning competition benchmarks. Compared to a standard delete relaxation heuristic, while the increased runtime overhead often is detrimental, in some cases the search space reduction is strong enough to result in improved performance overall.

The typical objective of path planning is to find the shortest feasible path. Many times, however, there may be no solution given the existence of constraints, such as obstacles. In these cases, the minimum constraint removal problem asks for the minimum set of constraints that need to be removed from the state space to find a solution. Unfortunately, minimum constraint removal paths do not exhibit dynamic programming properties, i.e., subsets of optimum solutions are not necessarily optimal. Thus, searching for such solutions is computationally expensive. This leads to approximate methods, which balance the cost of computing a solution and its quality. This work investigates alternatives in this context and evaluates their performance in terms of such tradeoffs. Solutions that follow a bounded-length approach, i.e., searching for paths up to a certain length, seem to provide a good balance between minimizing constraints, computational cost and path length.

Moving Target Search (MTS) is a dynamic path planning problem, where an agent is trying to reach a moving entity with a minimum path cost. Problems of this nature can be found in video games and dynamic robotics, which require fast processing time (real time). In this work, we introduce a new algorithm for this problem - the Moving Target Search with Subgoal Graphs (MTSub). MTSub is based on environment abstraction and uses Subgoal Graphs to speed up the searches for a minimal cost route to the target. The algorithm is optimal with respect to the knowledge that the agent has during the search. Experimental results show that MTSub can be used in real-time applications (e.g., applications requiring 5 microseconds response time per step). The experiments compared MTSub to G-FRA*, which is the best known dynamic algorithm so far, showing that MTSub is up to 29 times faster in average time per step, and up to 186 times faster in maximum time per step.

Solving planning problems via translation to propositional satisfiability (SAT) is one of the most successful approaches to automated planning. An important aspect of this approach is the encoding, i.e., the construction of a propositional formula from a given planning problem instance. Numerous encoding schemes have been proposed in the recent years each aiming to outperform the previous encodings on the majority of the benchmark problems. In this paper we take a different approach. Instead of trying to develop a new encoding that is better for all kinds of benchmarks we take recently developed specialized encoding schemes and design a method to automatically select the proper encoding for a given planning problem instance. In the paper we also examine ranking heuristics for the Relaxed Relaxed Exists-Step encoding, which plays an important role in our algorithm. Experiments show that our new approach significantly outperforms the state-of-the-art encoding schemes when compared on the benchmarks of the 2011 International Planning Competition.

We address planning with always preferences in propositional domains, proposing a new compilation schema for translating a STRIPS problem enriched with always preferences (and possibly also soft goals) into a STRIPS problem with action costs. Our method allows many STRIPS planners to effectively address planning with always preferences and soft goals. An experimental analysis indicates that such basic planners are competitive with current planners using techniques specifically developed to handle always preferences.