Linear temporal logic (LTL) is an expressive language that allows specifying temporally extended goals and preferences. A general approach to dealing with general LTL properties in planning is by ``compiling them away''; i.e., in a pre-processing phase, all LTL formulas are converted into simple, non-temporal formulas that can be evaluated in a planning state. This is accomplished by first generating a finite-state automaton for the formula, and then by introducing new fluents that are used to capture all possible runs of the automaton. Unfortunately, current translation approaches are worst-case exponential on the size of the LTL formula. In this paper, we present a polynomial approach to compiling away LTL goals. Our method relies on the exploitation of alternating automata. Since alternating automata are different from non-deterministic automata, our translation technique does not capture all possible runs in a planning state and thus is very different from previous approaches. We prove that our translation is sound and complete, and evaluate it empirically showing that it has strengths and weaknesses. Specifically, we find classes of formulas in which it seems to outperform significantly the current state of the art.

We address a spatial conservation planning problem in which the planner purchases a budget-constrained set of land parcels in order to maximize the expected spread of a population of an endangered species. Existing techniques based on the sample average approximation scheme and standard integer programming methods have high complexity and limited scalability. We propose a fast combinatorial optimization algorithm using Lagrangian relaxation and primal-dual techniques to solve the problem approximately. The algorithm provides a new way to address a range of conservation planning and scheduling problems. On the Red-cockaded Woodpecker data, our algorithm produces near optimal solutions and runs significantly faster than a standard mixed integer program solver. Compared with a greedy baseline, the solution quality is comparable or better, but our algorithm is 10–30 times faster. On synthetic problems that do not exhibit submodularity, our algorithm significantly outperforms the greedy baseline.

The paper faces the problem of plan repair in presence of numeric information, by providing a new method for the intelligent selection of numeric macro actions. The method relies on a generalization of deordering, extended with new conditions accounting for dependencies and threats implied by the numeric components. The deordering is used as a means to infer (hopefully) minimal ordering constraints then used to extract independent and informative macro actions. Each macro aims at compactly representing a sub-solution for the overall planning problem. To verify the feasibility of the approach, the paper reports experiments in various domains from the International Planning Competition% measuring the performance of the new strategy using two state of the art numeric planning systems; i.e., Colin Metric-FF. Results show (i) the competitiveness of the strategy in terms of coverage, time and quality of the resulting plans wrt current approaches, and (ii) the actual independence from the planner employed.

Nowadays, robots are programmed with a mix of discrete and continuous low level behaviors by experts in a very time consuming and expensive process. Existing automated planning approaches are either based on hybrid model predictive control techniques, which do not scale well due to time discretization, or temporal planners, which sacrifice plan expressivity by only supporting discretized fixed rates of change in continuous effects. We introduce Scotty, a mixed discrete-continuous generative planner that finds the middle ground between these two. Scotty can reason with linear time evolving effects whose behaviors can be modified by bounded control variables, with no discretization involved. Our planner exploits the expressivity of flow tubes, which compactly encapsulate continuous effects, and the performance of heuristic forward search. The generated solution plans are better suited for robust execution, as executives can use the flexibility in both time and continuous control variables to react to disturbances.

This work compares two actions' concurrency and co-occurrence employed in temporal modeling languages, one with a PDDL-style action modeling languages used by the AI planning community, exclusion mechanism, and another with an explicit and argue that they explain why MILP or SMT have notion of resources, and investigates their seemed unattractive. Specifically, we observe that PDDL 2.1 implications on constraint-based search. The first [Fox and Long, 2003] induces temporal gaps between consecutive mechanism forces temporal gaps in action schedules interdependent actions, and these gaps often induce and have a high performance penalty. The second twice the number of steps in the plans than what is necessary, mechanism avoids the gaps, with dramatically with strong negative performance implications. The gaps are improved performance.

Recently, a number of papers have shown that for many domains, using multiple heuristics in independent searches performs better than combining them into a single heuristic. Furthermore, using a large number of “weak” heuristics could potentially eliminate the need for the careful design of a few. The standard approach to distribute computation in these multi-heuristic searches is to rotate through the heuristics in a round-robin fashion. However, this strategy can be inefficient especially in the case when only a few of the heuristics are leading to progress. In this paper, we present two principled methods to adaptively distribute computation time among the different searches of the Multi- Heuristic A* algorithm. The first method, Meta-A*, constructs and searches a meta-graph, which represents the problem of finding the best heuristic as the problem of minimizing the total number of expansions. The second treats the scheduling of searches with different heuristics as a multi-armed bandit problem. It applies Dynamic Thompson Sampling (DTS) to keep track of what searches are making progress the most and continuously re-computes the schedule of searches based on this information. We provide a theoretical analysis and compare our new strategies with the round-robin method on a 12-DOF full-body motion planning problem and on sliding tile puzzle problems. In these experiments, we used up to 20 heuristics and observed a several times speedup without loss in solution quality.

The development of domain-independent planners This modular approach also supports the use of reformulation within the AI Planning community is leading to and configuration techniques which can automatically "off the shelf" technology that can be used in a reformulate, re-represent or tune the domain model and/or wide range of applications. Moreover, it allows a problem description in order to increase the efficiency of modular approach - in which planners and domain a planner and increase the scope of problems solved. The knowledge are modules of larger software applications idea is to make these techniques to some degree independent - that facilitates substitutions or improvements of domain and planner (that is, applicable to a range of individual modules without changing the of domains and planning engine technologies), and use them rest of the system. This approach also supports the to form a wrapper around a planner, improving its overall use of reformulation and configuration techniques, performance for the domain to which it is applied. Types which transform how a model is represented in order of reformulation include macro-learning [Botea et al., 2005; to improve the efficiency of plan generation. Newton et al., 2007], action schema splitting [Areces et al., In this paper, we investigate how the performance 2014] and entanglements [Chrpa and McCluskey, 2012]: here of planners is affected by domain model configuration.

Non-linear continuous change is common in real-world problems, especially those that model physical systems. We present an algorithm which builds upon existent temporal planning techniques based on linear programming to approximate non-linear continuous monotonic functions. These are integrated through a semantic attachment mechanism, allowing external libraries or functions that are difficult to model in native PDDL to be evaluated during the planning process. A new planning system implementing this algorithm was developed and evaluated. Results show that the addition of this algorithm to the planning process can enable it to solve a broader set of planning problems.

Abstraction is a powerful technique in search and planning. A fundamental problem of abstraction is that it can create spurious paths, i.e., abstract paths that do not correspond to valid concrete paths. In this paper, we define spurious paths as a generalization of spurious states. We show that spurious paths can be categorized into two types: state-independent spurious paths and state-specific spurious paths. We present a practical method that eliminates state-independent spurious paths, as well as state-specific spurious paths when integrated with mutex detection methods. We provide syntactical conditions under which our method can remove state-independent spurious paths completely. We demonstrate that eliminating spurious paths can improve a heuristic substantially, even in abstract spaces that are free of spurious states.

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.