Task allocation is ubiquitous in computer science and robotics, yet some problems have received limited attention in the computer science and AI community. Specifically, we will focus on multi-robot task allocation problems when tasks have time windows or ordering constraints. We will outline the main lines ofresearch and open problems.

Robots operating in the real world must be able to handle both discrete and continuous change. Many robot behaviors can be controlled through numeric parameters (called control variables), which affect the rate of the continuous change. Previous approaches capable of reasoning efficiently with control variables impose severe restrictions that limit the expressivity of the problems that can be solved. A broad class of robotic applications require, for example, convex quadratic constraints on state variables and control variables that are jointly constrained and that affect multiple state variables simultaneously. However, extensions to prior approaches are not straightforward, since these characteristics are non-linear and hard to scale. We introduce cqScotty, a heuristic forward search planner that solves these problems efficiently. While naive formulations of consistency checks are not convex and do not scale, cqScotty uses an efficient convex formulation, in the form of a Second Order Cone Program (SOCP), that is very fast to solve. We demonstrate the scalability of our approach on three new realistic domains.

Imagining the future helps anticipate and prepare for what is coming. This has great importance to many, if not all, human endeavors. In this paper, we develop the Planning Projector system prototype, which applies plan-recognition-as-planning technique to both explain the observations derived from analyzing relevant news and social media, and project a range of possible future state trajectories for human review. Unlike the plan recognition problem, where a set of goals, and often a plan library must be given as part of the input, the Planning Projector system takes as input the domain knowledge, a sequence of observations derived from the news, a time horizon, and the number of trajectories to produce. It then computes the set of trajectories by applying a planner capable of finding a set of high-quality plans on a transformed planning problem. The Planning Projector prototype integrates several components including: (1) knowledge engineering: the process of encoding the domain knowledge from domain experts; (2) data transformation: the problem of analyzing and transforming the raw data into a sequence of observations; (3) trajectory computation: characterizing the future state projection problem and computing a set of trajectories; (4) user interface: clustering and visualizing the trajectories. We evaluate our approach qualitatively and conclude that the Planning Projector helps users understand future possibilities so that they can make more informed decisions.

Automated planning can be used to efficiently recognize goals and plans from partial or full observed action sequences. In this paper, we propose goal recognition heuristics that rely on information from planning landmarks - facts or actions that must occur if a plan is to achieve a goal when starting from some initial state. We develop two such heuristics: the first estimates goal completion by considering the ratio between achieved and extracted landmarks of a candidate goal, while the second takes into account how unique each landmark is among landmarks for all candidate goals. We empirically evaluate these heuristics over both standard goal/plan recognition problems, and a set of very large problems. We show that our heuristics can recognize goals more accurately, and run orders of magnitude faster, than the current state-of-the-art.

Potential heuristics for state-space search are defined as weighted sums over simple state features. Atomic features consider the value of a single state variable in a factored state representation, while binary features consider joint assignments to two state variables. Previous work showed that the set of all admissible and consistent potential heuristics using atomic features can be characterized by a compact set of linear constraints. We generalize this result to binary features and prove a hardness result for features of higher dimension. Furthermore, we prove a tractability result based on the treewidth of a new graphical structure we call the context-dependency graph . Finally, we study the relationship of potential heuristics to transition cost partitioning . Experimental results show that binary potential heuristics are significantly more informative than the previously considered atomic ones.

Heuristics serve as a powerful tool in modern domain-independent planning (DIP) systems by providing critical guidance during the search for high-quality solutions. However, they have not been broadly used with hierarchical planning techniques, which are more expressive and tend to scale better in complex domains by exploiting additional domain-specific knowledge. Complicating matters, we show that for Hierarchical Goal Network (HGN) planning, a goal-based hierarchical planning formalism that we focus on in this paper, any poly-time heuristic that is derived from a delete-relaxation DIP heuristic has to make some relaxation of the hierarchical semantics. To address this, we present a principled framework for incorporating DIP heuristics into HGN planning using a simple relaxation of the HGN semantics we call Hierarchy-Relaxation. This framework allows for computing heuristic estimates of HGN problems using any DIP heuristic in an admissibility-preserving manner. We demonstrate the feasibility of this approach by using the LMCut heuristic to guide an optimal HGN planner. Our empirical results with three benchmark domains demonstrate that simultaneously leveraging hierarchical knowledge and heuristic guidance substantially improves planning performance.

Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of non-deterministic planning with temporally extended goals specified in linear temporal logic (LTL), interpreted over either finite or infinite traces. Unlike existing LTL planners, we place no restrictions on our LTL formulae beyond those necessary to distinguish finite from infinite interpretations. We generate plans by compiling LTL temporally extended goals into problem instances described in the Planning Domain Definition Language that are solved by a state-of-the-art fully observable non-deterministic planner. We propose several different compilations based on translations of LTL to (Büchi) alternating or (Büchi) non-deterministic finite state automata, and evaluate various properties of the competing approaches. We address a diverse spectrum of LTL planning problems that, to this point, had not been solvable using AI planning techniques, and do so in a manner that demonstrates highly competitive performance.

It has been shown recently that the performance of greedy best-first search (GBFS) for computing plans that are not necessarily optimal can be improved by adding forms of exploration when reaching heuristic plateaus: from random walks to local GBFS searches. In this work, we address this problem but using structural exploration methods resulting from the ideas of width-based search. Width-based methodsseek novel states, are not goal oriented, and their power has been shown recently in the Atari and GVG-AI video-games. We show first that width-based exploration in GBFS is more effective than GBFS with local GBFS search (GBFS-LS), and then proceed to formulate a simple and general computational framework where standard goal-oriented search (exploitation) and width-based search (structural exploration) are combined to yield a search scheme, best-first width search, that is better than both and which results in classical planning algorithms that outperform the state-of-the-art planners.

Interaction between multiple agents requires some form of coordination and a level of mutual awareness. When computers and robots interact with people, they need to recognize human plans and react appropriately. Plan and goal recognition techniques have focused on identifying an agent's task given a sufficiently long action sequence. However, by the time the plan and/or goal are recognized, it may be too late for computing an interactive response. We propose an integration of planning with probabilistic recognition where each method uses intermediate results from the other as a guiding heuristic for recognition of the plan/goal in-progress as well as the interactive response. We show that, like the used recognition method, these interaction problems can be compiled into classical planning problems and solved using off-the-shelf methods. In addition to the methodology, this paper introduces problem categories for different forms of interaction, an evaluation metric for the benefits from the interaction, and extensions to the recognition algorithm that make its intermediate results more practical while the plan is in progress.

Axioms can be used to model derived predicates in domain-independent planning models. Formulating models which use axioms can sometimes result in problems with much smaller search spaces than the original model. We propose a method for automatically extracting a particular class of axioms from standard STRIPS PDDL models. More specifically, we identify operators whose effects become irrelevant given some other operator, and generate axioms that capture this relationship. We show that this algorithm can be used to successfully extract axioms from standard IPC benchmark instances, and show that the extracted axioms can be used to significantly improve the performance of an IP-based planner.