Distributed POMDPs provide an expressive framework for modeling multiagent collaboration problems, but NEXP-Complete complexity hinders their scalability and application in real-world domains. This paper introduces a subclass of distributed POMDPs, and TREMOR, an algorithm to solve such distributed POMDPs. The primary novelty of TREMOR is that agents plan individually with a single agent POMDP solver and use social model shaping to implicitly coordinate with other agents. Experiments demonstrate that TREMOR can provide solutions orders of magnitude faster than existing algorithms while achieving comparable, or even superior, solution quality.

This paper describes a novel and competitive complete conformant planner. Key to the enhanced performance is an efficient encoding of belief states as disjunctive normal form formulae and an efficient procedure for computing the successor belief state. We provide experimental comparative evaluation on a large pool of benchmarks. The novel design provides great efficiency and enhanced scalability, along with the intuitive structure of disjunctive normal form representations.

Nowadays many urban areas have been equipped with networks of surveillance cameras, which can be used for automatic localization and tracking of people. However, given the large resource demands of imaging sensors in terms of bandwidth and computing power, processing the image streams of all cameras simultaneously might not be feasible. In this paper, we consider the problem of dynamical sensor selection based on user-defined objectives, such as maximizing coverage or improved localization uncertainty.  We propose a decision-theoretic approach modeled as a POMDP, which selects k sensors to consider in the next time frame, incorporating all observations made in the past. We show how, by changing the POMDP's reward function, we can change the system's behavior in a straightforward manner, fulfilling the user's chosen objective. We successfully apply our techniques to a network of 10 cameras.

An essential quality of a good partner is her responsiveness to other team members. Recent work in dynamic plan execution exhibits elements of this quality through the ability to adapt to the temporal uncertainties of others agents and the environment. However, a good teammate also has the ability to adapt on-the-fly through task assignment. We generalize the framework of dynamic execution to perform plan execution with dynamic task assignment as well as scheduling. This paper introduces Chaski, a multi-agent executive for scheduling temporal plans with online task assignment. Chaski enables an agent to dynamically update its plan in response to disturbances in task assignment and the schedule of other agents. The agent then uses the updated plan to choose, schedule and execute actions that are guaranteed to be temporally consistent and logically valid within the multi-agent plan. Chaski is made efficient through an incremental algorithm that compactly encodes all scheduling policies for all possible task assignments. We apply Chaski to perform multi-manipulator coordination using two Barrett Arms within the authors' hardware testbed. We empirically demonstrate up to one order of magnitude improvements in execution latency and solution compactness compared to prior art.

Planning based on propositional SAT(isfiability) is a powerful approach to computing step-optimal plans given a parallel execution semantics. In this setting: (i) a solution plan must be minimal in the number of plan steps required, and (ii) non-conflicting actions can be executed instantaneously in parallel at a plan step. Underlying SAT-based approaches is the invocation of a decision procedure on a SAT encoding of a bounded version of the problem. A fundamental limitation of existing approaches is the size of these encodings. This problem stems from the use of a direct representation of actions — i.e. each action has a corresponding variable in the encoding. A longtime goal in planning has been to mitigate this limitation by developing a more compact split — also termed lifted — representation of actions in SAT encodings of parallel step-optimal problems. This paper describes such a representation. In particular, each action and each parallel execution of actions is represented uniquely as a conjunct of variables. Here, each variable is derived from action pre and post- conditions . Because multiple actions share conditions , our encoding of the planning constraints is factored and relatively compact. We find experimentally that our encoding yields a much more efficient and scalable planning procedure over the state-of-the-art in a large set of planning benchmarks.

Heuristic forward search is the dominant approach to satisficing planning to date. Most successful planning systems, however, go beyond plain heuristic search by employing various search-enhancement techniques.  One example is the use of helpful actions or preferred operators, providing information which may complement heuristic values.  A second example is deferred heuristic evaluation, a search variant which can reduce the number of costly node evaluations. Despite the wide-spread use of these search-enhancement techniques however, we note that few results have been published examining their usefulness. In particular, while various ways of using, and possibly combining, these techniques are conceivable, no work to date has studied the performance of such variations.  In this paper, we address this gap by examining the use of preferred operators and deferred evaluation in a variety of settings within best-first search. In particular, our findings are consistent with and help explain the good performance of the winners of the satisficing tracks at IPC 2004 and 2008.

This paper presents a generic anytime forward-search constraint-based algorithm for solving planning problems expressed in the CNT framework (Constraint Network on Timelines). It is generic because it allows many kinds of search to be covered, from complete tree search to greedy search. It is anytime because some parameter settings, together with domain-specific knowledge, allow high quality plans to be produced very quickly and to be further improved. It is forward because it systematically considers the decisions to be made in a chronological order. It is finally constraint-based because it is built on top of the CNT framework which is an extension of the CSP framework able to model discrete event dynamic systems and because it is implemented on top of the Choco constraint programming tool from which it inherits all the constraint handling machinery. Experimental comparisons are made in terms of quality profile with other domain-dependent and domain-independent planners.

We consider real-time planning problems in which some states are unsolvable, i.e., have no path to a goal. Such problems are difficult for real-time planning algorithms such as RTA* in which all states must be solvable. We identify a property called k-safeness, in which the consequences of a bad choice become apparent within k moves after the choice is made. When k is not too large, this makes it possible to identify unsolvable states in real time. We provide a modified version of RTA* that is provably complete on all k -safe problems. We derive k -safeness conditions for real-time deterministic versions of the well-known Tireworld and Racetrack domains, and provide experimental results showing that our modified version of RTA* works quite well in these domains.

Planning means deciding on the future course of action based on predictions of what will happen when an activity is carried out in one way or the other. As we apply action planning to autonomous, sensor-guided mobile robots with manipulators or even to humanoid robots we need very realistic and detailed predictions of the behavior generated by a plan in order to improve the robot's performance substantially. In this paper we investigate the high-fidelity temporal projection of realistic robot behavior based on physics- and sensor-based simulation systems. We equip a simulator and interpreter with means to log simulated plan executions into a database. A logic-based query and inference mechanism then retrieves and reconstructs the necessary information from the database and translates the information into a first-order representation of robot plans and the behavior they generate.  The query language enables the robot planning system to infer the intentions, the beliefs, and the world state at any projected time.  It also allows the planning system to recognize, diagnose, and analyze various plan failures typical for performing everyday manipulation tasks.

This paper considers Just-In-Time Job-Shop Scheduling, in which each activity has an earliness and a tardiness cost with respect to a due date. It proposes a constraint programming approach, which includes a novel filtering algorithm and dedicated heuristics. The filtering algorithm uses a machine relaxation to produce a lower bound that can be obtained by solving a Just-In-Time Pert problem. It also includes pruning rules which update the variable bounds and detect precedence constraints. The paper presents experimental results which demonstrate the effectiveness of the approach over a wide range of benchmarks.