Author name cluster

Martim Brandao

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

JAIR Journal 2026 Journal Article

Generalised Merge and Shrink Abstractions for Temporal Planning

Martim Brandao
Amanda Coles
Andrew Coles
Rebecca Eifler

Temporal planning is a hard problem that requires good heuristic and memoization strategies to solve efficiently. Merge-and-shrink abstractions have been shown to serve as effective heuristics for classical planning, but it is still unclear how to implement merge-and-shrink in the temporal domain and how effective the method is in this setting. In this paper we propose a method to compute merge-and-shrink abstractions for general temporal planning problems, in a way that is applicable to both partial- and total-order temporal planners. We extend a previous publication to allow the formalism to apply to temporal problems with non-compression safe actions, in particular through the use of a classical planning surrogate of a temporal planning task. The method relies on pre-computing heuristics as formulas of temporal variables that are evaluated at search time, and it allows to use standard merging, shrinking and pruning strategies. Compared to state-of-the-art Relaxed Planning Graph heuristics, we show that the method leads to improvements in coverage, computation time, and number of expanded nodes to solve optimal problems, as well as leading to improvements in unsolvability-proving of problems with deadlines, and the time to compute Minimally Unsolvable Goal Subsets (MUGS). We exhaustively test the method over these problems and various usage settings, showing improvements in coverage of up to 53%, computation time up to 60%, and expanded nodes up to 75%.

PDF Details DOI

AAMAS Conference 2022 Conference Paper

Explainability in Multi-Agent Path/Motion Planning: User-study-driven Taxonomy and Requirements

Martim Brandao
Masoumeh Mansouri
Areeb Mohammed
Paul Luff
Amanda Coles

Multi-Agent Path Finding (MAPF) and Multi-Robot Motion Planning (MRMP) are complex problems to solve, analyze and build algorithms for. Automatically-generated explanations of algorithm output, by improving human understanding of the underlying problems and algorithms, could thus lead to better user experience, developer knowledge, and MAPF/MRMP algorithm designs. Explanations are contextual, however, and thus developers need a good understanding of the questions that can be asked about algorithm output, the kinds of explanations that exist, and the potential users and uses of explanations in MAPF/MRMP applications. In this paper we provide a first step towards establishing a taxonomy of explanations, and a list of requirements for the development of explainable MAPF/MRMP planners. We use interviews and a questionnaire with expert developers and industry practitioners to identify the kinds of questions, explanations, users, uses, and requirements of explanations that should be considered in the design of such explainable planners. Our insights cover a diverse set of applications: warehouse automation, computer games, and mining.

PDF