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.

We compare five any-angle path-planning algorithms, Theta*, Block A*, Field D*, ANYA, and Any-Angle Subgoal Graphs in terms of solution quality and runtime. Any-angle path-planning is a fairly new research area, and no direct comparison exists between these algorithms. We implement each algorithm from scratch and use similar implementations to provide a fair comparison.

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.

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.

Monte-Carlo Tree Search (MCTS) algorithms estimate the value of MDP states based on rewards received by performing multiple random simulations. MCTS algorithms can use different strategies to aggregate these rewards and provide an estimation for the states’ values. The most common aggregation method is to store the mean reward of all simulations. Another common approach stores the best observed reward from each state. Both of these methods have complementary benefits and drawbacks. In this paper, we show that both of these methods are biased estimators for the real expected value of MDP states. We propose an hybrid approach that uses the best reward for states with low noise, and otherwise uses the mean. Experimental results on the Sailing MDP domain show that our method has a considerable advantage when the rewards are drawn from a noisy distribution.

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.

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.

Merge-and-shrink (M&S) is a framework to generate abstraction heuristics for cost-optimal planning. A recent approach computes simulation relations on a set of M&S abstractions in order to identify states that are better than others. This relation is then used for pruning states in the search when a "better" state is already known. We propose the usage of simulation relations inside the M&S framework in order to detect irrelevant transitions in abstract state spaces. This potentially simplifies the abstraction allowing M&S to derive more informed heuristics. We also tailor M&S to remove irrelevant operators from the planning task. Experimental results show the potential of our approach to construct well-informed heuristics and simplify the planning tasks prior to the search.

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.

Fork decoupling is a recent approach to exploiting problem structure in state space search. The problem is assumed to take the form of a fork, where a single (large) center component provides preconditions for several (small) leaf components. The leaves are then conditionally independent in the sense that, given a fixed center path p, the compliant leaf moves - those leaf moves enabled by the preconditions supplied along p - can be scheduled independently for each leaf. Fork-decoupled state space search exploits this through conducting a regular search over center paths, augmented with maintenance of the compliant paths for each leaf individually. We herein show that the same ideas apply to much more general star-topology structures, where leaves may supply preconditions for the center, and actions may affect several leaves simultaneously as long as they also affect the center. Our empirical evaluation in planning, super-imposing star topologies by automatically grouping the state variables into suitable components, shows the merits of the approach.