For Web service composition, choreography has recently received great attention and demonstrated a few key advantages over orchestration such as distributed control, fairness, data efficiency, and scalability. Automated design of choreography plans, especially distributed plans for multiple roles, is more complex and has not been studied before. Existing work requires manual generation assisted by model checking. In this paper, we propose a novel planning-based approach that can automatically convert a given composition task to a distributed choreography specification. Although planning has been used for orchestration, it is difficult to use planning for choreography, as it involves decentralized control, concurrent workflows, and contingency. We propose a few novel techniques, including compilation of contingencies, dependency graph analysis, and communication control, to handle these characteristics using planning. We theoretically show the correctness of this approach and empirically evaluate its practicability.

Activity planning for missions such as the Mars Exploration Rover mission presents many technical challenges, including oversubscription, consideration of time, concurrency, resources, preferences, and uncertainty. These challenges have all been addressed by the research community to varying degrees, but significant technical hurdles still remain. In addition, the integration of these capabilities into a single planning engine remains largely unaddressed. However, I argue that there is a deeper set of issues that needs to be considered -- namely the integration of planning into an iterative process that begins before the goals, objectives, and preferences are fully defined. This introduces a number of technical challenges for planning, including the ability to more naturally specify and utilize constraints on the planning process, the ability to generate multiple qualitatively different plans, and the ability to provide deep explanation of plans.

The currently dominant approach to domain-independent planning is planning as heuristic search, with most successful planning heuristics being based on solutions to delete-relaxed versions of planning problems, in which the negative effects of actions are ignored. We introduce a principled, flexible, and practical technique for augmenting delete-relaxed tasks with a limited amount of delete information, by introducing special fluents that explicitly represent conjunctions of fluents in the original planning task. Differently from previous work, conditional effects are used to limit the growth of the task to be linear in the number of such conjunctions, making its use for obtaining heuristic functions feasible. The resulting heuristics are empirically evaluated, and shown to be some- times much more informative than standard delete-relaxation heuristics.

Creating software agents that learn interactively requires the ability to learn from a small number of trials, extracting general, flexible knowledge that can drive behavior from observation and interaction. We claim that qualitative models provide a useful intermediate level of causal representation for dynamic domains, including the formulation of strategies and tactics. We argue that qualitative models are quickly learnable, and enable model-based reasoning techniques to be used to recognize, operationalize, and construct more strategic knowledge. This paper describes an approach to incrementally learning qualitative influences by demonstration in the context of a strategy game. We show how the learned model can help a system play by enabling it to explain which actions could contribute to maximizing a quantitative goal. We also show how reasoning about the model allows it to reformulate a learning problem to address delayed effects and credit assignment, such that it can improve its performance on more strategic tasks such as city placement.

While founded on the situation calculus, current implementations of Golog are mainly based on the closed-world assumption or its dynamic versions or the domain closure assumption. Also, they are almost exclusively based on regression. In this paper, we propose a first-order interpreter for knowledge-based Golog with sensing based on exact progression and limited reasoning. We assume infinitely many unique names and handle first-order disjunctive information in the form of the so-called proper+ KBs. Our implementation is based on the progression and limited reasoning algorithms for proper+ KBs proposed by Liu, Lakemeyer and Levesque. To improve efficiency, we implement the two algorithms by grounding via a trick based on the unique name assumption. The interpreter is online but the programmer can use two operators to specify offline execution for parts of programs. The search operator returns a conditional plan, while the planning operator is used when local closed-world information is available and calls a modern planner to generate a sequence of actions.

General Game Playing (GGP) agents must be capable of playing a wide variety of games skillfully. Monte-Carlo Tree Search (MCTS) has proven an effective reasoning mechanism for this challenge, as is reflected by its popularity among designers of GGP agents. Providing GGP agents with the knowledge relevant to the game at hand in real time is, however, a challenging task. In this paper we propose two enhancements for MCTS in the context of GGP, aimed at improving the effectiveness of the simulations in real time based on in-game statistical feedback. The first extension allows early termination of lengthy and uninformative simulations while the second improves the action-selection strategy when both explored and unexplored actions are available. The methods are empirically evaluated in a state-of-the-art GGP agent and shown to yield an overall significant improvement in playing strength.

We study a two-player pursuit-evasion game, in which an agent moving amongst obstacles is to be maintained within ``sight" of a pursuing robot. Using a discretization of the environment, our main contribution is to design an efficient algorithm that decides, given initial positions of both pursuer and evader, if the evader can take any moving strategy to go out of sight of the pursuer at any time instant. If that happens, we say that the evader wins the game. We analyze the algorithm, present several optimizations and show results for different environments. For situations where the evader cannot win, we compute, in addition, a pursuit strategy that keeps the evader within sight, for every strategy the evader can take. Finally, if it is determined that the evader wins, we compute its optimal escape trajectory and the corresponding optimal pursuit trajectory.

In this paper, we consider the problem of feasibly integrating new pick-up and delivery requests into existing vehicle itineraries in a dynamic, dial-a-ride problem (DARP) setting. Generalizing from previous work in oversubscribed task scheduling, we define a controlled iterative repair search procedure for finding an alternative set of vehicle itineraries in which the overall solution has been feasibly extended to include newly received requests. We first evaluate the performance of this technique on a set of DARP feasibility benchmark problems from the literature. We then consider its use on a real-world DARP problem, where it is necessary to accommodate all requests and constraints must be relaxed when a request cannot be feasibly integrated. For this latter analysis, we introduce a constraint relaxation post processing step and consider the performance impact of using our controlled iterative search over the current greedy search approach.

We consider the problem of computing optimal generalised policies for relational Markov decision processes. We describe an approach combining some of the benefits of purely inductive techniques with those of symbolic dynamic programming methods. The latter reason about the optimal value function using first-order decision theoretic regression and formula rewriting, while the former, when provided with a suitable hypotheses language, are capable of generalising value functions or policies for small instances. Our idea is to use reasoning and in particular classical first-order regression to automatically generate a hypotheses language dedicated to the domain at hand, which is then used as input by an inductive solver. This approach avoids the more complex reasoning of symbolic dynamic programming while focusing the inductive solver's attention on concepts that are specifically relevant to the optimal value function for the domain considered.

A model of story generation recently proposed by Riedl and Young casts it as planning, with the additional condition that story characters behave intentionally. This means that characters have perceivable motivation for the actions they take. I show that this condition can be compiled away (in more ways than one) to produce a classical planning problem that can be solved by an off-the-shelf classical planner, more efficiently than by Riedl and Young's specialised planner.