Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps. Continuous-time CBS (CCBS) is a recently proposed version of CBS that guarantees optimal solutions without the need to discretize time. However, the scalability of CCBS is limited because it does not include any known improvements of CBS. In this paper, we begin to close this gap and explore how to adapt successful CBS improvements, namely, prioritizing conflicts (PC), disjoint splitting (DS), and high-level heuristics, to the continuous time setting of CCBS. These adaptions are not trivial, and require careful handling of different types of constraints, applying a generalized version of the Safe interval path planning (SIPP) algorithm, and extending the notion of cardinal conflicts. We evaluate the effect of the suggested enhancements by running experiments both on general graphs and $2^k$-neighborhood grids. CCBS with these improvements significantly outperforms vanilla CCBS, solving problems with almost twice as many agents in some cases and pushing the limits of multiagent path finding in continuous-time domains.

We propose techniques for efficiently determining optimal solutions to large logistics planning domain problems. We map a problem instance to a directed graph and show that no more than one vehicle per weakly connected component of the graph is needed for an optimal solution. We propose techniques for efficiently finding the vehicles which must be employed for an optimal solution. Also we develop a strong admissible heuristic based on the analysis of a directed graph, the cycles of which represent situations in the problem state in which a vehicle must visit a location more than once. To the best of our knowledge, ours is the first method that determines optimal solutions for large logistics instances (including the largest instances in the IPC 1998 and IPC 2000 problem sets).

Temporal logics allow to formulate and reason about the development A unified formalism for these techniques would offer two of logic-based systems, for example about paths main advantages: decoupling the derivation and exploitation in factored state spaces. These are for instance common in of information and easily combining different sources planning, where temporal logics have always been present. of information. As one extreme, the entire planning task can be specified in a Currently the derivation and exploitation of information temporal logic language and plans are generated by theorem are integrated in most cases: someone proposes a new source proving (Koehler and Treinen 1995) or model construction of information and shows how it can correctly be exploited (Cerrito and Mayer 1998).

Delete-free planning underlies many popular relaxation (h+) based heuristics used in state-of-the-art planners; it provides a simpler setting for exploring new pruning methods and other ideas; and a number of interesting recent planning domains are naturally delete-free. In this paper we explore new pruning methods for planning in delete-free planning domains. First, we observe that optimal delete-free plans can be composed from contiguous sub-plans that focus on one fact landmark at a time. Thus, instead of attempting to achieve the goal, the planner can focus on more easily achievable landmarks at each stage. Then, we suggest a number of complementary pruning techniques that are made more powerful with this observation. To carry out these pruning techniques efficiently, we make heavy use of an And/Or graph depicting the planning problem. We empirically evaluate these ideas using the FD framework, and show that they lead to clear improvements.

Automated planning is a well studied research topic thanks to its wide range of real-world applications. Despite significant progress in this area many planning problems still remain hard and challenging. Some techniques such as learning macro-operators improve the planning process by reformulating the (original) planning problem. While many encouraging practical results have been derived from such reformulation methods, little attention has been paid to the theoretical properties of reformulation such as soundness, completeness, and algorithmic complexity. In this paper we build up a theoretical framework describing reformulation schemes such as action elimination or creating macro-actions. Using this framework, we show that finding entanglements (relationships useful for action elimination) is as hard as planning itself. Moreover, we design a tractable algorithm for checking under what conditions it is safe to reformulate a problem by removing primitive operators (assembled to a macro-operator).

Recently, Richter et al. [2008] proposed a novel some point in every solution plan. Previous work way of using a set of landmarks as a pseudo-heuristic within has very successfully exploited planning landmarks a satisficing heuristic search. This technique allowed both in satisficing (non-optimal) planning. We propose a reducing the length of the generated plans, as well as improving methodology for deriving admissible heuristic estimates success rate both with respect to the iterative approach for cost-optimal planning from a set of planning of Hoffmann et al., and with respect to other stateof-the-art landmarks. The resulting heuristics fall into a satisficing planners. In particular, the LAMA planner novel class of multi-path dependent heuristics, and by Richter and Westphal utilizing such a landmarks-based we present a simple best-first search procedure exploiting heuristic search was the clear winner of the Sequential Satisficing such heuristics.