Browsing by Author "Geffner, Héctor"

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  • Bonet, Blai; Geffner, Héctor (Association for the Advancement of Artificial Intelligence (AAAI), 2012)
    In the presence of non-admissible heuristics, A* and other best-first algorithms can be converted into anytime optimal algorithms over OR graphs, by simply continuing the search after the first solution is found. The ...
  • Geffner, Héctor (IOS Press, 2014)
    Artificial Intelligence is a brain child of Alan Turing and his universal programmable computer. During the 1960s and 1970s, AI researchers used computers for exploring intuitions about intelligence and for writing programs ...
  • Kominis, Filippos (Universitat Pompeu Fabra, 2017-12-01)
    Classical planning is the problem of finding a sequence of actions that achieve a desired goal from an initial state, assuming deterministic actions. Dynamic epistemic logic (DEL) on the other hand, provides formal frameworks ...
  • Vidal, Vincent; Geffner, Héctor (Elsevier, 2006)
    A key feature of modern optimal planners such as graphplan and blackbox is their ability to prune large parts of the search space. Previous Partial Order Causal Link (POCL) planners provide an alternative branching scheme ...
  • Ferrer Mestres, Jonathan (Universitat Pompeu Fabra, 2018-05-09)
    Planning in robotics is often split into task and motion planning. The task planner decides what needs to be done, while the motion planner fills up geometric details. However, such a decomposition is not effective in general ...
  • Patrizini, Fabio; Lipovetzky, Nir; Giacomo, Giuseppe de; Geffner, Héctor (Association for the Advancement of Artificial Intelligence (AAAI), 2011)
    Classical planning has been notably successful in synthesizing finite plans to achieve states where propositional goals hold. In the last few years, classical planning has also been extended to incorporate temporally ...
  • Francès, Guillem (Universitat Pompeu Fabra, 2017-10-26)
    Classical planning is concerned with finding sequences of actions that achieve a certain goal from an initial state of the world, assuming that actions are deterministic, states are fully known, and both are described ...
  • Ramírez Jávega, Miquel; Geffner, Héctor (Association for the Advancement of Artificial Intelligence (AAAI), 2011)
    Plan recognition is the problem of inferring the goals and plans of an agent from partial observations of her behavior. Recently, it has been shown that the problem can be formulated and solved using/nplanners, reducing ...
  • Kolobov, Andrey; Mausam; Weld, Daniel S.; Geffner, Héctor (Association for the Advancement of Artificial Intelligence (AAAI), 2011)
    Research in efficient methods for solving infinite-horizon MDPs has so far concentrated primarily on discounted MDPs and the more general stochastic shortest path problems (SSPs). These are MDPs with 1) an optimal value ...
  • Bonet, Blai; Geffner, Héctor (Elsevier, 2008)
    The automatic derivation of heuristic functions for guiding the search for plans is a fundamental technique in planning. The type of heuristics that have been considered so far, however, deal only with simple planning ...
  • Bonet, Blai; Geffner, Héctor (Association for the Advancement of Artificial Intelligence (AAAI), 2005)
    We describe the version of the GPT planner to be used in the planning competition. This version, called mGPT, solves mdps specified in the ppddl/nlanguage by extracting and using different classes of lower bounds, along ...
  • Keyder, Emil Ragip (Universitat Pompeu Fabra, 2010-12-17)
    Classical planning is the problem of nding a sequence of actions that take an agent from an initial state to a desired goal situation, assuming deter- ministic outcomes for actions and perfect information. Satis cing ...
  • Ramírez Jávega, Miquel (Universitat Pompeu Fabra, 2012-05-17)
    Plan recognition is the problem of inferring the goals and plans of an agent after partially observing its behavior. This is the inverse of planning, the problem of finding the actions that need to be done in order to ...
  • Bonet, Blai; Geffner, Héctor (Elsevier, 2001)
    In the AIPS98 Planning Contest, the hsp planner showed that heuristic search planners can be competitive with state-of-the-art Graphplan and sat planners. Heuristic search planners like hsp transform planning problems into ...
  • Bonet, Blai; Geffner, Héctor (Association for the Advancement of Artificial Intelligence (AAAI), 2011)
    Planning with partial observability can be formulated as a non-deterministic search problem in belief space. The problem is harder than classical planning as keeping track of beliefs is harder than keeping track of states, ...
  • Francès, Guillem; Ramírez Jávega, Miquel; Lipovetzky, Nir; Geffner, Héctor (International Joint Conferences on Artificial Intelligence Organization (IJCAI), 2017)
    Classical planning is concerned with problems where a goal needs to be reached from a known initial state by doing actions with deterministic, known effects. Classical planners, however, deal only with classical problems ...
  • Lipovetzky, Nir; Geffner, Héctor (Association for the Advancement of Artificial Intelligence (AAAI), 2011)
    We define a probe to be a single action sequence computedgreedily from a given state that either terminates in the goalor fails. We show that by designing these probes carefullyusing a number of existing and new polynomial ...
  • Francès, Guillem; Geffner, Héctor (IJCAI & AAAI Press, 2016)
    Existentially quantified variables in goals and action preconditions are part of the standard PDDL planning language, yet few planners support them, while those that do compile them away at an exponential cost. In this ...
  • Lipovetzky, Nir (Universitat Pompeu Fabra, 2012-12-07)
    Classical planning is the problem of finding a sequence of actions for achieving a goal from an initial state assuming that actions have deterministic effects. The most effective approach for finding such plans is based ...
  • Albore, Alexandre (Universitat Pompeu Fabra, 2012-02-22)
    Artificial Intelligence Planning is about acting in order to achieve a desired goal. Under incomplete information, the task of finding the actions needed to achieve the goal can be modelled as a search problem in the ...