PIERRE LISON THESIS

The belief models must therefore incorporate mecha- nisms for computing and adapting these saliency measures over time. 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. The components can be either unmanaged data-driven or managed goal- driven. The first aspect pertains to the use of machine learning techniques to learn the model parameters. My PhD defense is officially scheduled for Friday, 21st February at A percept union is just another belief, whose content is the combination of all the features from the included percepts.

An entity can be an object, a place, a landmark, a person, etc. Multi-modal information should be fused. I am delighted to visit the Idiap research institute in Switzerland for a period of 3 months from August to October in order to work together with Andrei Popescu-Belis on dialogue modelling for statistical machine translation. The binder is directly connected to the other subarchi- tectures i. The incorporation of spatio-temporal framing is thus necessary to express when and where a particular belief is supposed to hold. Lifted first-order belief propagation. In gen- eral, each alternative value can be expressed as a propositional logical for- mula.

Pierre Lison Completes Doctoral Degree

SAFERS investigates how speech technology and machine learning can be used to automatically transcribe emergency calls in Norway and provide real-time analysis and prediction tools to emergency response services.

For a given agent a, the epistemic status e can be either: First, the newly created belief is compared to the already existing beliefs for similarity. The resulting network is illustrated in the Figure 1.

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Sound and efficient inference with probabilistic and deterministic dependencies.

pierre lison thesis

perre Planning and acting with an integrated sense of space. Notice that the parti- tion function Z grows exponentially with the weights, and will tend to infinity for large values of w1 or w2.

Section 2 provides a brief intro- duction to Markov Logic, the framework used for belief modelling.

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Perceptual beliefs have by construction no belief parent. Parameters such as recency have to be taken into account, in order to discard outdated observations.

Statistics for the knowledge economy. This is the well-known engineering problem of multi-target, multi-sensor data fusion [7].

pierre lison thesis

In Von der Form zur Bedeutung: 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 thesia holds. Machine Learning, 62 The implementation of the approach outlined in this report is ongoing.

Notice that the location is described as a pointer to another belief k. As such, it provides an elegant account of both the uncertainty and complex- ity of situated human-robot interactions. Section 3 starts by describing the software architecture in which our pierre is being integrated.

The probability of a false positive is 0. The prior probability of a grouping is also specified as a Markov Logic formula.

pierre lison thesis

Pierre Lison holds a M. Beliefs also incorporate various contextual information such as spatio-temporal framing, multi-agent epistemic status, and saliency measures. The first question to address is how these high-level repre- sentations can be reliably abstracted from low-level sensory data [1,27]. Engineering intelligent information-processing systems with cast.

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In our model, the salience is defined as a real-valued measure which combines several perceptual measures such as the object size and its linear and angular distances relative to the robot.

Pierre Lison Completes Doctoral Degree – Department of Informatics

Such algorithms are crucial to provide an upper bound on the system latency and thus preserve its efficiency and tractability.

Since these two rules admit a few exceptions profes- sors can be on sabbatical, and some undergraduates can teach as assistantsthey are specified as soft constraints with finite weights w1 and w2. The main purpose of this project was to predict future bycatch for performing cost effective regulations.

Spoken language interaction with model uncertainty: Association for Computational Linguistics. Each subarchitecture encapsulates a number of processing components run- ning in parallel.

This is done by constructing a new belief which is linked via a pointer to the one of the referred-to entity. Anal- ogous to perceptual grouping which seeks to bind observations over modalities, tracking seeks to bind beliefs over time.

Generation and evaluation of user tailored responses in multimodal dialogue. Now, off to a week of well-deserved vacation ;- [] A-magasinet featured this week a three-pages article on Lenny and my research on human-robot interaction through spoken dialogue.

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