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Browsing by Author "Rens, G"

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  • Rens, G; Ferrein, A; Van der Poel, E (2009-06)
    Traditionally, agent architectures based on the Belief-Desire-Intention (BDI) model make use of precompiled plans, or if they do generate plans, the plans do not involve stochastic actions nor probabilistic observations. ...
  • Rens, G; Ferrein, A (Centre for Artificial Intelligence Research, 2013-09)
    We consider online partially observable Markov decision processes (POMDPs) which compute policies by local look-ahead from the current belief-state. One problem is that belief-nodes deeper in the decision-tree increase in ...
  • Rens, G; Ferrein, A; van der Poel, E (PRASA, 2008-11)
    For sophisticated robots, it may be best to accept and reason with noisy sensor data, instead of assuming complete observation and then dealing with the effects of making the assumption. We shall model uncertainties with ...
  • Rens, G; Lakemeyer, G; Meyer, T (IOS Press, 2012-08)
    We propose a non-standard modal logic for specifying agent domains where the agent’s actuators and sensors are noisy, causing uncertainty in action and perception. The logic is multi-modal, indexed with actions; the logic ...
  • Rens, G; Meyer, T; Ferrein, A; Lakemeyer, G (2011-07)
    The authors propose a novel modal logic for specifying agent domains where the agent’s actuators and sensors are noisy, causing uncertainty in action and perception. The logic draws both on POMDP theory and logics of action ...
  • Rens, G; Meyer, T; Lakemeyer, G (Springer, 2014-03)
    We present a logic inspired by partially observable Markov decision process (POMDP) theory for specifying agent domains where the agent's actuators and sensors are noisy (causing uncertainty). The language features modalities ...
  • Rens, G; Meyer, T (AAAI Publications, 2015-05)
    One way for an agent to deal with uncertainty about its beliefs is to maintain a probability distribution over the worlds it believes are possible. A belief change operation may recommend some previously believed worlds ...
  • Rens, G; Meyer, T; Casini, G (IOS Press, 2016-08)
    We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae ...
  • Rens, G (Association for the Advancement of Artificial Intelligence, 2016-04)
    I propose a framework for an agent to change its probabilistic beliefs when a new piece of propositional information a is observed. Traditionally, belief change occurs by either a revision process or by an update process, ...
  • Rens, G; Meyer, T; Lakemeyer, G (COMMONSENSE 2013, 2013-05)
    We investigate the requirements for specifying the behaviors of actions in a stochastic domain. That is, we propose how to write sentences in a logical language to capture a model of probabilistic transitions due to the ...
  • Rens, G; Meyer, T; Casini, G (Association for the Advancement of Artificial Intelligence, 2016-04)
    We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae ...
  • Rens, G; Meyer, T; Lakemeyer, G (Elsevier, 2014-06)
    A logic for specifying probabilistic transition systems is presented. Our perspective is that of agents performing actions. A procedure for deciding whether sentences in this logic are valid is provided. One of the main ...
  • Rens, G (Scitepress Digital Library, 2015-01)
    A novel algorithm to speed up online planning in partially observable Markov decision processes (POMDPs) is introduced. I propose a method for compressing nodes in belief-decision-trees while planning occurs. Whereas ...