PIERRE LISON THESIS

Remember me on this computer. Key to our approach is the use of Markov Logic as a unified representation formal- ism. And finally, in complement to information refinement, beliefs are also abstracted by constructing high-level, amodal sym- bolic representations from low-level perceptual i. This is the well-known engineering problem of multi-target, multi-sensor data fusion [7]. Log In Sign Up. Now that the software architecture of our approach has been described, the next section proceeds by detailing how beliefs are represented in the binder, and how this representation is precisely formalised.

A grouping of two percepts will be given a high probability if 1 one or more feature pairs correlate with each other, and 2 there are no incompatible feature pairs. In practice, maintaining a single big distribution over all possible values of the belief is both computationally expensive and unecessary. This is realised by checking whether their respective features correlate with each other. They therefore serve as a representational backbone for a wide range of high-level cognitive capabilities related to reasoning, planning and learning in complex and dynamic environ- ments. For a pair of two percepts p1 and p2 , we infer the likelihood of these two percepts being generated from the same underlying entity in the real-world. Situated resolution and generation of spatial referring expressions for robotic assistants. The components can be either unmanaged data-driven or managed goal- driven.

Figure 5 illustrates the role of these categories in the belief formation process.

By Jeremy L Wyatt. A blue mug colour, location, and height. The information flow between components is thus based on the idea of parallel refinement of shared representations, eschewing the standard point-to-point connectivity of traditional message-based frameworks.

Following the fusion operation, beliefs are then gradually refined — new, improved esti- mations pietre derived for each belief feature, given the collection of knowledge sources which have been merged. Now that the software architecture of our approach has been described, the next section proceeds by detailing how beliefs are represented in the binder, and how this representation is precisely formalised.

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The graphical representation of ML,C contains a node for each ground pred- icate. A framework for grounding language in action and perception. Via this central working memory, each component is able to asynchronously read and update shared information within the subarchitecture.

Left unfiltered, the quantity of sensory informa- tion to process is sure to exhaust its computational resources. The components can be either unmanaged data-driven or managed goal- driven. Bottom-up belief model formation.

pierre lison thesis

It allows us to guide the attentional behaviour of the agent by specifying which entities are currently in focus. The structure and parameters of the constructed network will vary depending on the set of constants provided to ground the predicates of the Markov Logic formulae. They include a frame stating where and when the information is assumed to be valid, and an epistemic status stating for which agent s the information holds.

A the end of the process, a percep- tual belief is created, with four features: Perceptual beliefs have by construction no belief parent. The probability of a false positive is 0. The research in his PhD was mainly dedicated to Bayesian spatio-temporal modeling of bycatch in the Barents Sea shrimp fishery, and was conducted in collaboration with the Norwegian Marine Research Institute.

Following 1 and 2the joint probability distribution of a ground Markov network ML,C is then given by: This in- cludes both the evolution of the physical environment, and the evolution of the interaction itself.

pierre lison thesis

Its main purpose is to serve as the voice of young researchers in the public arena, through a range of initiatives related to research policy and science dissemination.

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At NR, Pierre will, among other topics, continue his postdoctoral project and work on personalized marketing as part of the Centre for Research-based Pifrre Big Insight.

pierre lison thesis

And finally, in complement to information refinement, beliefs are also abstracted by constructing high-level, amodal sym- bolic representations from low-level perceptual i. The value of liosn node is true iff the ground predicate is true.

Pierre Lison Completes Doctoral Degree

Its expressive power allows us to capture both the rich relational structure of the environment and the uncertainty arising pieere the noise and incompleteness of low-level sensory data. As such, it provides an elegant account of both the uncertainty and complex- ity of situated human-robot interactions. Thanks to everyone who contributed to this event [ 123video ].

Second, the utterance also provides new information — namely that the object is yellow. Situated resolution and generation of spatial referring expressions for robotic assistants. Markov Logic is a combination of first-order logic and probabilistic modelling.

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Notice that the parti- tion function Z grows exponentially with the weights, and will tend to infinity for large values of w1 or w2. The belief B2 is selected as most likely referent.

Most of these beliefs will have a near-zero prob- ability. Log In Sign Up.