We present a new paradigm for planning by learning, where the planner is given a model of the world and  a small set of states of interest, but no indication of optimal actions in these states. The additional information can help focus the planner on regions of the state space that are of interest and lead to improved performance. We demonstrate this idea by introducing novel model-checking reduction operations for First Order Decision Diagrams (FODD), a representation that has been used to implement decision-theoretic planning with Relational Markov Decision Processes (RMDP). Intuitively, these reductions modify the construction of the value function by removing any complex specifications that are irrelevant to the set of training examples, thereby focusing on the region of interest. We show that such training examples can be constructed on the fly from a description of the planning problem thus we can bootstrap to get a self-taught planning system. Additionally, we provide a new heuristic to embed universal and conjunctive goals within the framework of RMDP planners, expanding the scope and applicability of such systems. We show that these ideas lead to significant improvements in performance in terms of both speed and coverage of the planner, yielding state of the art planning performance on problems from the International Planning Competition.

Factored planning methods aim to exploit locality to efficiently solve large but "loosely coupled" planning problems by computing solutions locally and propagating limited information between components. However, all factored planning methods presented so far work with representations that require certain parameters to be bounded (e.g. number of coordination points between local plans considered); the satisfaction of those bounds by a given problem instance is difficult to establish a priori, and the influence of those parameters on the problem complexity is unclear. We present an instance of the factored planning framework using a representation of the (regular) sets of local plans by finite automata, which does not require any such bound. By substituting weighted automata, we can even do factored cost-optimal planning. We test an implementation of the method on the few standard planning benchmarks that we have found to be amenable to factoring. We show that this method runs in polynomial time under conditions similar to those considered in previous work, but not only under those conditions. Thus, what constitutes an essential measure of "factorability" remains obscure.

In this talk, I will introduce computer-aided algorithm design and discuss its main ingredients: design patterns, which provide ways of structuring potentially large spaces of candidate algorithms, and meta-algorithmic optimisation procedures, which are used for finding good designs within these spaces. After explaining how this algorithm design approach differs from and complements related approaches in program synthesis, genetic programming and so-called hyperheuristics, I will illustrate its success using examples from our own work in SAT-based software verification (Hutter et al. 2007), timetabling (Chiarandini, Fawcett, and Hoos 2008) and mixed integer programming (Hutter, Hoos, and Leyton-Brown 2010). Furthermore, I will argue why this approach can be expected to be particularly useful and effective for building better solvers for rich and diverse classes of combinatorial problems, such as planning and scheduling. Finally, I will outline out how programming by optimisation — a design paradigm that emphasises the automated construction of performance-optimised algorithm by means of searching large spaces of alternative designs — has the potential to transform the design of high-performance algorithm from a craft that is based primarily on experience and intuition into a principled and highly effective engineering effort.

Planning as Satisfiability is a most successful approach to optimal propositional planning. It draws its strength from the efficiency of state-of-the-art propositional satisfiability solvers, combined with the utilization of constraints that are inferred from the problem planning graph. One of the recent improvements of the framework is the addition of long-distance mutual exclusion (londex) constraints that relate facts and actions which refer to different time steps. In this paper we compare different encodings of planning as satisfiability wrt the constraint propagation they achieve in a modern SAT solver. This analysis explains some of the differences observed in the performance of different encodings, and leads to some interesting conclusions. For instance, the Blackbox encoding achieves more propagation than the one of Satplan06, and therefore is a stronger formulation of planning as satisfiability. Moreover, our investigation suggests a new more compact and stronger model for the problem. We prove that in this new formulation many of the londex constraints are redundant in the sense that they do not add anything to the constraint propagation achieved by the model. Experimental results suggest that the theoretical results obtained are practically relevant.

Anytime search algorithms solve optimisation problems by quickly finding a (usually suboptimal) first solution and then finding improved solutions when given additional time. To deliver an initial solution quickly, they are typically greedy with respect to the heuristic cost-to-go estimate h. In this paper, we show that this low-h bias can cause poor performance if the greedy search makes early mistakes. Building on this observation, we present a new anytime approach that restarts the search from the initial state every time a new solution is found. We demonstrate the utility of our method via experiments in PDDL planning as well as other domains, and show that it is particularly useful for problems where the heuristic has systematic errors.

Forward State Planning with Reachability Heuristics is arguably the most successful approach to Automated Planning up to date. In addition to an estimation of the distance to the goal, relaxed plans obtained with such heuristics provide the search with useful information such as helpful actions and look-ahead states. However, this information is extracted only from the beginning of the relaxed plan. In this paper, we propose using information extracted from the last actions in the relaxed plan to generate intermediate goals backwards. This allows us to use information from previous computations of the heuristic and reduce the depth of the search tree.

We present Shopper, a plan execution engine that facilitates experimental evaluation of plans and makes it easier for planning researchers to incorporate replanning. Shopper interprets the LTML plan language, which extends PDDL in two major ways: with more expressive control structures, and with support for semantic web services modeled on OWL-S. LTML's command structures include not only conventional ones such as branching, iteration, and procedure calls, but also features needed to handle HTN plans, such as precondition-filtered method choice. Unlike conventional programming languages, LTML supports interaction with the agent's belief store, so that its execution semantics line up with those assumed by planners. LTML actions extend PDDL actions in having outputs as well as effects, which means that they can support actions that sense the world; an important special case of this is semantic web services, which reveal information about a state hidden from the agent. To support experimentation as well as action in the real world, Shopper accommodates multiple, swappable implementations of its primitive action API. For example, one may interact with real web services through SOAP and WSDL, or with simulated web services through local procedure calls. We describe novel features of LTML, the interpretation strategy, swappable back-ends, and the implementation.

Goal utility dependencies arise when the utility of achieving a goal depends on the other goals that are achieved with it. This complicates the planning procedure because achieving a new goal can potentially alter the utilities of all the other goals currently achieved. In this paper, we present an encoding procedure that enables general-purpose Max-SAT solvers to be used to solve planning problems with goal utility dependencies. We compare this approach to one using integer programming via an empirical evaluation using benchmark problems from past international planning competitions. Our results indicate that this approach is competitive and sometimes more successful than an integer programming one -- solving two to three times more subproblems in some domains, while being outperformed by only a significantly smaller margin in others.

We present an extension of the planning framework based on action graphs and local search to deal with PDDL2.1 temporal problems requiring concurrency, while previously the approach could only solve problems admitting a sequential solution. The paper introduces a revised plan representation supporting concurrency and some new search techniques using it, which are implemented in a new version of the LPG planner. An experimental analysis indicates that the proposed approach is suitable to temporal planning with requiring concurrency and is competitive with state-of-the-art planners.

We present a planning domain that encodes the problem of generating natural language sentences. This domain has a number of features that provoke fairly unusual behavior in planners. In particular, hitherto no existing automated planner was sufficiently effective to be of practical value in this application. We analyze in detail the reasons for ineffectiveness in FF, resulting in a few minor implementation fixes in FF's preprocessor, and in a basic reconfiguration of its search options. The performance of the modified FF is up to several orders of magnitude better than that of the original FF, and for the first time makes automated planners a practical possibility for this application. Beside thus highlighting the importance of preprocessing and automated configuration techniques, we show that the domain still poses several interesting challenges to the development of search heuristics.