In bounded-suboptimal heuristic search, the aim is to find a solution path within a given bound as quickly as possible, which is crucial when computat...
The proposed algorithm, Dynamic Suboptimality Weighted A* (DSWA*), addresses a critical challenge in bounded-suboptimal heuristic search: the static n...
In modern automation settings, jobs are processed across machines with interdependencies and are subject to limited equipment availability. When trans...
The paper "Multi-Agent Path Finding for Schedule Constrained Automation" introduces MAPF-SC, an extension of Multi-Agent Path Finding (MAPF) designed...
This extended abstract presents new empirical results of recently introduced Critical Section Macro-operators (CSMs) whose design is inspired by using...
This extended abstract presents timely new empirical results concerning Critical Section Macro-operators (CSMs), a concept inspired by the use of lock...
The problem of traffic signal optimisation has been successfully tackled using the PDDL+ planning formalism, which also provides an ideal ground for s...
This extended abstract presents a timely and relevant exploration into the core dilemma faced when applying advanced AI planning techniques, specifica...
In this paper, we investigate the application of heuristics based on Graph Neural Networks (GNNs) to lifted numeric planning problems, an area that ha...
This extended abstract presents a timely investigation into the application of Graph Neural Networks (GNNs) for learning heuristic functions in the co...
It is well known that numeric planning can be made decidable if the domain of all numeric state variables is finite. This bounded formulation can be p...
This extended abstract, titled "BLAST: Bit-Blasting Numbers for Classical Planning," tackles a crucial gap between theoretical tractability and practi...
Combinatorial problems abound in industry. A persistent issue encountered using search-based solutions is that evaluating particular nodes may be expe...
This extended abstract presents a compelling approach to tackling computationally expensive combinatorial optimization problems, specifically focusing...
BAE*, and the independently developed DIBBS, are state-of-the-art bidirectional heuristic search algorithms that exploit heuristic consistency to effi...
This position paper tackles a critical efficiency concern within state-of-the-art bidirectional heuristic search algorithms, particularly BAE* and DIB...
Encoding combinatorial problems in terms of propositional satisfiability (SAT) enables utilization of highly efficient SAT solvers for combinatorial s...
This position paper introduces a compelling and novel approach to enhance SAT local search through the specialized application of Large Language Model...
Multi-Agent Path Finding (MAPF) deals with finding conflict-free paths for a set of agents from an initial configuration to a given target configurati...
This position paper critically examines a fundamental inefficiency in current approaches to Lifelong Multi-Agent Path Finding (LMAPF), specifically th...
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