Planification par fusions incrémentales de graphes
In this paper, we introduce a generic and fresh model for distributed planning called "Distributed Planning Through Graph Merging" ({\sf DPGM}). This model unifies the different steps of the distributed planning process into a single step. Our approach is based on a planning graph structure for the agent reasoning and a CSP mechanism for the individual plan extraction and the coordination. We assume that no agent can reach the global goal alone. Therefore the agents must cooperate, {\it i.e.,} take in into account potential positive interactions between their activities to reach their common shared goal. The originality of our model consists in considering as soon as possible, {\it i.e.,} in the individual planning process, the positive and the negative interactions between agents activities in order to reduce the search cost of a global coordinated solution plan.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Hypergraph Echo State Network
A hypergraph as a generalization of graphs records higher-order interactions among nodes, yields a more flexible network model, and allows non-linear features for a group of nodes. In this article, we propose a hypergrap…
Binary ClassificationMod\'elisation bay\'esienne de la planification motrice des gestes de parole: \'Evaluation du r\^ole des diff\'erentes modalit\'es sensorielles (Bayesian modeling of speech gesture motor planning: Evaluating the role of different sensory modalities )
La prise en compte des informations auditives et proprioceptives dans le contr{\^o}le de la parole est mise en {\'e}vidence par un nombre croissant de r{\'e}sultats exp{\'e}rimentaux. Cependant, les mod{\`e}les de produc…
Tâches auxiliaires pour l’analyse biaffine en graphes de dépendances (Auxiliary tasks to boost Biaffine Semantic Dependency Parsing)
L’analyseur biaffine de Dozat & Manning (2017), qui produit des arbres de dépendances syntaxiques, a été étendu avec succès aux graphes de dépendances syntaxico-sémantiques (Dozat & Manning, 2018). Ses performances sur l…
Dependency ParsingSemantic Dependency ParsingORION: Teaching Language Models to Reason Efficiently in the Language of Thought
Large Reasoning Models (LRMs) achieve strong performance in mathematics, code generation, and task planning, but their reliance on long chains of verbose "thinking" tokens leads to high latency, redundancy, and incoheren…
Reinforcement LearningCode GenerationUn algorithme d’analyse sémantique fondée sur les graphes via le problème de l’arborescence généralisée couvrante (A graph-based semantic parsing algorithm via the generalized spanning arborescence problem)
Nous proposons un nouvel algorithme pour l’analyse sémantique fondée sur les graphes via le problème de l’arborescence généralisée couvrante.
Semantic Parsing