We define the robustness of a sequential plan as the probability that it will execute successfully despite uncertainty in the execution environment. We consider a rich notion of uncertainty over continuous domains that includes stochastic action effects, and changes to state variables due to unpredictable exogenous events. Given a characterization of this uncertainty in terms of probability distributions (e.g., Gaussian) our contributions are two-fold: First, we describe a novel approach to computing the robustness of a plan in the situation calculus, which (a) separates the projection problem from the problem of reasoning about probability, and (b) explicitly reveals the relevance and statistical independence of random variables and events (i.e., conditions that contain random variables). Then, building on this approach, we describe a forward search based planner that generates maximally robust plans, exploiting the revealed structure for speed-up. Preliminary empirical results demonstrate that our approach can realize exponential savings in both time and space compared to the classical sampling approach.

The growing presence of household robots in inhabited environments arises the need for new robot task planning techniques. These techniques should take into consideration not only the actions that the robot can perform or unexpected external events, but also the actions performed by a human sharing the same environment, in order to improve the cohabitation of the two agents, e.g., by avoiding undesired situations for the human. In this paper, we present a human-aware planner able to address this problem. This planner supports alternative hypotheses of the human plan, temporal duration for the actions of both the robot and the human, constraints on the interaction between robot and human, partial goal achievement and, most importantly, the possibility to use observations of human actions in the policy generated for the robot. The planner has been tested as a standalone component and in conjunction with our framework for human-robot interaction in a real environment.

The FF relaxed plan heuristic is one of the most effective techniques in domain-independent satisficing planning and is used by many state-of-the-art heuristic-search planners. However, it may sometimes provide quite inaccurate information, since its relaxation strategy, which ignores the delete effects of actions, may oversimplify a problem's structure. In this paper, we propose a novel algorithm for computing relaxed plans which — although still relaxed — aim at respecting much of the structure of the original problem. We accomplish this by generating  relaxed plans with a reduced number of conflicts. An action a will add a conflict when added to a relaxed plan if the resulting plan is provably illegal (i.e, not executable) in the un-relaxed problem. As a second contribution, we propose a new lookahead strategy, in the spirit of Vidal's YAHSP lookahead, that can better exploit the contents of relaxed plans. In our experimental analysis, we show that the resulting heuristic improves over the FF heuristic in a number of domains, most notably when lookahead is enabled. Moreover, the resulting system, which uses our new lookahead, is competitive with state-of-the-art planners, and even better in terms of the number of solved problems.

Recent research in robot exploration and mapping has focused on sampling environmental hotspot fields. This exploration task is formalized by Low, Dolan, and Khosla (2008) in a sequential decision-theoretic planning under uncertainty framework called MASP. The time complexity of solving MASP approximately depends on the map resolution, which limits its use in large-scale, high-resolution exploration and mapping. To alleviate this computational difficulty, this paper presents an information-theoretic approach to MASP (iMASP) for efficient adaptive path planning; by reformulating the cost-minimizing iMASP as a reward-maximizing problem, its time complexity becomes independent of map resolution and is less sensitive to increasing robot team size as demonstrated both theoretically and empirically. Using the reward-maximizing dual, we derive a novel adaptive variant of maximum entropy sampling, thus improving the induced exploration policy performance. It also allows us to establish theoretical bounds quantifying the performance advantage of optimal adaptive over non-adaptive policies and the performance quality of approximately optimal vs. optimal adaptive policies. We show analytically and empirically the superior performance of iMASP-based policies for sampling the log-Gaussian process to that of policies for the widely-used Gaussian process in mapping the hotspot field. Lastly, we provide sufficient conditions that, when met, guarantee adaptivity has no benefit under an assumed environment model.

We provide a theoretical analysis of planning via Petri net unfolding, a novel technique for synthesising parallel plans. Parallel plans are generally valued for their execution flexi- bility, which manifests as alternative choices for the order- ing of operators and potentially faster plan executions. Being a relatively new approach, the flexibility properties of plans synthesised via unfolding, and even the concurrency seman- tics supported by this technique, are particularly unclear and only understood at an informal level. In this paper, we first formally characterise the concurrency semantics of planning via unfolding as a further restriction on the standard notion of independence. More importantly, we then prove that plans obtained using this approach are optimal deorderings and op- timal reorderings in terms of the number of ordering con- straints on operators and plan execution time, respectively. These results provide objective guarantees on the quality of plans obtained by the unfolding technique.

Solving real-world problems using symbolic planning often requires a simplified formulation of the original problem, since certain subproblems cannot be represented at all or only in a way leading to inefficiency. For example, manipulation planning may appear as a subproblem in a robotic planning context or a packing problem can be part of a logistics task. In this paper we propose an extension of PDDL for specifying semantic attachments. This allows the evaluation of grounded predicates as well as the change of fluents by externally specified functions. Furthermore, we describe a general schema of integrating semantic attachments into a forward-chaining planner and report on our experience of adding this extension to the planners FF and Temporal Fast Downward. Finally, we present some preliminary experiments using semantic attachments.

A planning problem is k- dependent if each action has at most k pre-conditions on variables unaffected by the action. This concept is well-founded since k is a constant for all but a few of the standard planning domains, and is known to have implications for tractability. In this paper, we present several new complexity results for P ( k ), the class of k- dependent planning problems with binary variables and polytree causal graphs. The problem of plan generation for P ( k ) is equivalent to determining how many times each variable can change. Using this fact, we present a polytime plan generation algorithm for P (2) and P (3). For constant k > 3, we introduce and use the notion of a cover to find conditions under which plan generation for P ( k ) is polynomial.

In this paper we realize the synthesis of continuous coordinations By envisaging standards to publish and access services over based on the conceptual framework of (Pistore, the Web, the Service-Oriented Computing (SOC) paradigm Traverso, and Bertoli 2005), which recasts the composition promises a novel degree of interoperability between distributed problem in terms of planning; namely, we act at its core applications that realize business processes. One by adopting a very simple, yet expressive requirements language, cornerstone of SOC stands in the provision of novel and and devising a novel planning algorithm. In particular, more complex business logics by the coordination of existing the requirement language expresses coordination constraints services. Due to the complexity of manually realizing that are transformed into preference-ordered maintenability such coordinations, automatedly supporting the synthesis goals, and the algorithm deals with such goals in of service orchestrations is crucial to the actual enactment the presence of exogenous events (which encode independent of SOC. This problem is extremely hard since, asynchronous evolutions of services).

In order to scale with modern processors, planning algorithms must become multi-threaded. In this paper, we present parallel shared-memory algorithms for two problems that underlie many planning systems: suboptimal and anytime heuristic search. We extend a recently-proposed approach for parallel optimal search to the suboptimal case, providing two new pruning rules for bounded suboptimal search. We also show how this new approach can be used for parallel anytime search. Using temporal logic, we prove the correctness of our framework, and in an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problems using an 8-core machine, we show that it yields faster search performance than previous proposals.

Model-based planning often presumes a static system model, while in a practice physical system may evolve or drift over time. This paper proposes the idea of pervasive model adaptation in a production system, where the model is dynamically updated using observation of production output. The core idea is the interplay between model adaptation and production planning. We seek plans which simultaneously serve the goals of achieving high productivity for production, and information gathering for model adaptation. We use a modular printing example to illustrate issues such as formulation of the information criterion and search strategy for informative plans. The idea of pervasive adaptation can be further extended to improve long term productivity in production systems.