To keep up with a continuously changing environment, all operations performed on the belief models content updates, queries, etc. The first public release of the OpenDial toolkit is finally available! The Markov Logic Network for tracking works as follows. Partially observable markov decision processes for spoken dialog systems. Salience-driven contextual priming of speech recognition for human-robot interaction.
The rest of this report is structured as follows. Figure 5 provides a graphical illustration of this process. Continual processing of situated dialogue in human-robot collaborative activities. Cognitive Science, 28 5: Section 5 then explains step-by-step how such beliefs can be constructed bottom-up, iteratively, from perceptual inputs. In speech-based HRI, critical tasks in dialogue understanding, management and production are directly dependent on such belief models to prime or guide their internal processing operations.
Section 6 provides additional details on the attention and filtering systems. Given the problem complexity we just outlined, exact inference is unfeasible.
In the following, we briefly review the definition of Markov networks, and then show how they can be generated from a Markov logic network L.
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Log In Sign Up. We specify such information in the epistemic status of the belief. Proceedings of the IEEE, 85 1: The distribution can usually be decomposed into a list of smaller distributions over parts of the belief content. This section explores these two related issues.
The belief models must therefore incorporate mecha- nisms for computing and adapting these saliency measures over time. Belief modelling for situation awareness in human-robot interaction.
Such epistemic distinc- tions need to be explicitly encoded in the belief representations.
The Markov Logic Network for temporal smoothing is similar to the one used for multi-modal fusion: This is the well-known engineering problem of multi-target, multi-sensor data fusion .
The outcome is a set of possible unions, each of which has an existence probability describing the likelihood of the grouping.
This con- stitutes our sixth and final requirement. A filtering system can effectively prune such unecessary beliefs.
Statistics for the knowledge economy. The project website also features a user manual for the toolkit. In speech-based HRI, critical tasks in dialogue understanding, management and production are directly dependent on such belief models to prime or guide their internal processing operations.
The architectural schema is based on a distributed set of subarchitectures. Reference resolution is performed via a Markov Logic Network specifying the correlations between the linguistic constraints of the referring expression and the belief features — in particular, the entity saliency and its associated cate- gorical knowledge.
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. We are using the Alchemy software 4 for efficient probabilistic inference.
Pierre Lison Completes Doctoral Degree
Beliefs evolve dynamically over time, and are constructed by a three-fold iterative process of information fusion, refinement and abstraction.
The value of the node is true iff the ground predicate is true.
PhD thesis, MIT, Key to our approach is the use of a first-order probabilistic language, Markov Logic , as a uni- fied representation formalism to construct rich, multi-modal models of context.
The incorporation of spatio-temporal framing is thus necessary to express when and where a particular belief is supposed to hold. This situation awareness is generally expressed in some sort of belief models in which various aspects of pieerre external reality are encoded.
Pierre Lison Completes Doctoral Degree – Department of Informatics
Taskar, editors, Introduction to Statistical Relational Learning. Pierre worked in various areas of machine learning and language technology, such as human-robot interaction, conversational interfaces, search technology, machine translation and text mining. Finally, subarchitectures can also communicate with each other by accessing reading, inserting, updating their respective working memories.
Five central requirements can be formulated: A sub- formula such as: This awareness is usually encoded in some implicit or explicit represen- tation of the situated context. Instead, they include in their belief history a pointer to the local data structure in the subarchitec- ture which was at the origin of the belief.