Section 2 provides a brief intro- duction to Markov Logic, the framework used for belief modelling. Given the problem complexity we just outlined, exact inference is unfeasible. A the end of the process, a percep- tual belief is created, with four features: Given the requirements of our application domain see Section 1 , and particularly the need to operate under soft real-time constraints, such approximation methods are an absolute necessity. The beliefs can then be directly exploited by high-level cognitive functions such as planning, cross-modal learning or communication. This enables the system to deal with varying levels of noice and uncertainty, which are pervasive and unavoidable for most sensory-motric processes.
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. Improving the accuracy and efficiency of MAP inference for markov logic. My paper on model-based Bayesian reinforcement learning for dialogue management has been accepted for the upcoming http: 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. Naturally, a combination of both mechanisms is also possible. We specify such information in the epistemic status of the belief.
These beliefs models are spatio-temporally framed and include epistemic information for multi-agent settings. The project website also features a user manual for the toolkit.
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lisob 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. For more information contact: Markov Logic is a combination of first-order logic and probabilistic modelling.
Our approach departs from previous work such as  or  by introducing a much richer modelling of multi- modal beliefs.
Artificial Intelligence, The outcome is a set of possible unions, pierfe of which has an existence probability describing the likelihood of the grouping.
Learning words and syntax for a scene description task.
Pierre Lison Completes Doctoral Degree
Representation, reasoning, and relational structures: We are using the Alchemy software 4 for efficient probabilistic inference. In Perception and Interactive Technologies: Finally, Section 8 concludes this report, and provides directions for future work. Situated resolution and generation of spatial referring expressions for robotic assistants.
At NR, Pierre will, among other topics, continue his postdoctoral project and work on personalized marketing as part of the Centre for Research-based Innovation Big Insight. An edge between two nodes signifies that the corresponding ground atoms appear together in at least one grounding of one formula in L.
The weight of the thesus corresponds to the weight wi associated with Fi.
Such algorithms are crucial to provide an upper bound on the system latency and thus preserve its efficiency and tractability. For a pair of two percepts p1 and p2we infer the likelihood of these two percepts being generated from the same underlying entity in the real-world.
They can also be used by perceptual components to adapt their internal processing operations to the current situated context contextual priming, anticipation, etc.
The probability of these correlations are encoded in a Markov Logic Network. Abstraction in perceptual symbol systems.
An introduction to multisensor data fusion. Philosophical Transactions of the Royal Society of London: In speech-based HRI, critical tasks in dialogue understanding, management and production are directly dependent on such belief thesus to prime or guide their internal processing operations.
Using the notion of perspective, we can capture the fact that each agent view the environment in its own specific way i. Such Markov network represents a probability distribution over possible words.
Given the problem complexity we just outlined, exact inference is unfeasible. Producing contextually appropriate intonation in an information-state based dialogue system.
The article is essentially a summary of my PhD work and describes the formalisation of probabilistic rules, the statistical estimation of unknown parameters, and the empirical evaluation of the framework in a human-robot interaction domain.
Left unfiltered, the quantity of sensory informa- tion to process is sure to exhaust its computational resources.
Pierre Lison Completes Doctoral Degree – Department of Informatics
Anal- ogous to perceptual grouping which seeks to bind observations over modalities, tracking seeks to bind beliefs over time.
Computer Speech and Language, 21 2: Thanks to everyone who contributed to this event [ 123video ].
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