publications([{
   "lang": "fr",
   "publisher": "Lavoisier",
   "doi": "http://doi.org/10.3166/ria.29.11-46",
   "uri": "http://iihm.imag.fr/publication/MFC15a/",
   "title": "Planification flexible. Un besoin en intelligence ambiante. Un défi en planification automatique",
   "bibtype": "article",
   "journal": "Revue d'Intelligence Artificielle (RIA)",
   "year": 2015,
   "number": 1,
   "pages": "11-46",
   "volume": 29,
   "id": 720,
   "abbr": "MFC15a",
   "authors": {
      "1": {
         "first_name": "Cyrille",
         "last_name": "Martin"
      },
      "2": {
         "first_name": "Humbert",
         "last_name": "Fiorino"
      },
      "3": {
         "first_name": "Gaëlle",
         "last_name": "Calvary"
      }
   },
   "date": "2015-03-30",
   "type": "Revues nationales avec comité de lecture",
   "abstract": "In order to be used in Ambiant Intelligence, automated planning has to generate highlevel\r\naction plans involving control structures for execution controlers, which role is to play\r\nthese plans with respect to events perceived in the environment. In our approach of \"flexible\"\r\nplanning, environment non determinism is managed by plan control structures. In this paper,\r\nwe explore how to express and to generate high-level plans for deterministic planning problems\r\nby defining the operational and denotational semantics of new operators for plan composition.\r\nOur Lambda GraphPlan (LGP) planner incorporates into plans iterations representing nondeterministic\r\nchoices among a set of resources subject to the same abstract treatment. LGP is a planning-graph based algorithm that extracts patterns of actions whose scheduling is indifferent\r\nwith respect to goal reachability and aggregates them into iterative structures. We show\r\nthat LGP can be highly efficient when the solution plans incorporate iterative structures.",
   "type_publi": "revcomlec"
}]);