She has (which includes a human user), while planning determines equipped the apartment with a series of service robots, the concrete actions that should be carried out in order to sensors and actuators which help her manage some of best support the perceived context. The domain description the physical and cognitive difficulties she has due to formalism used by SAM is based on metric temporal constraints; her age. Her home alerts her if she appears to be overcooking such domains model both the criteria for context inference her meals, and autonomously organizes when and the planning operators used for plan synthesis. The of the user and to contextually synthesize action plans for home recognizes when Malin is sleeping, eating and actuators in the intelligent environment. The knowledge representation scheme used in SAM is based State of the art robotic and sensor systems can be leveraged on Allen's Interval Relations (Allen 1984), augmented with to achieve intelligent functionalities that are useful in a number temporal bounds.

She has (which includes a human user), while planning determines equipped the apartment with a series of service robots, the concrete actions that should be carried out in order to sensors and actuators which help her manage some of best support the perceived context. The domain description the physical and cognitive difficulties she has due to formalism used by SAM is based on metric temporal constraints; her age. Her home alerts her if she appears to be overcooking such domains model both the criteria for context inference her meals, and autonomously organizes when and the planning operators used for plan synthesis. The of the user and to contextually synthesize action plans for home recognizes when Malin is sleeping, eating and actuators in the intelligent environment. The knowledge representation scheme used in SAM is based State of the art robotic and sensor systems can be leveraged on Allen's Interval Relations (Allen 1984), augmented with to achieve intelligent functionalities that are useful in a number temporal bounds.

She has (which includes a human user), while planning determines equipped the apartment with a series of service robots, the concrete actions that should be carried out in order to sensors and actuators which help her manage some of best support the perceived context. The domain description the physical and cognitive difficulties she has due to formalism used by SAM is based on metric temporal constraints; her age. Her home alerts her if she appears to be overcooking such domains model both the criteria for context inference her meals, and autonomously organizes when and the planning operators used for plan synthesis. The of the user and to contextually synthesize action plans for home recognizes when Malin is sleeping, eating and actuators in the intelligent environment. The knowledge representation scheme used in SAM is based State of the art robotic and sensor systems can be leveraged on Allen's Interval Relations (Allen 1984), augmented with to achieve intelligent functionalities that are useful in a number temporal bounds.

We seek to better understand the difference in quality of the several publicly released embeddings. We propose several tasks that help to distinguish the characteristics of different embeddings. Our evaluation of sentiment polarity and synonym/antonym relations shows that embeddings are able to capture surprisingly nuanced semantics even in the absence of sentence structure. Moreover, benchmarking the embeddings shows great variance in quality and characteristics of the semantics captured by the tested embeddings. Finally, we show the impact of varying the number of dimensions and the resolution of each dimension on the effective useful features captured by the embedding space. Our contributions highlight the importance of embeddings for NLP tasks and the effect of their quality on the final results.

The availability of massive amounts of raw domain data has created an urgent need for sophisticated AI systems with capabilities to find complex and useful information in big-data repositories in real-time. Such systems should have capabilities to process and extract significant information from natural language documents, search and answer complex questions, make sophisticated predictions about future events, and generally interact with users in much more powerful and intuitive ways. To be effective, these systems need a significant amount of domain-specific knowledge in addition to the general-domain knowledge. Ontologies/Knowledge-Bases represent knowledge about domains of interest and serve as the backbone for semantic technologies and applications. However, creating such domain models is time consuming, error prone, and the end product is difficult to maintain. In this paper, we present a novel methodology to automatically build semantically rich knowledge models for specific domains using domain-relevant unstructured data from resources such as web articles, manuals, e-books, blogs, etc. We also present evaluation results for our automatic ontology/knowledge-base generation methodology using freely-available textual resources from the World Wide Web.

The difference between typical instances and atypical instances in a natural categorization process has been introduced by E. Rosh and studied by cognitive psychology and AI. A lot of the knowledge representation systems are expressed in using fuzzy concepts but a degree of membership raises some problem for natural categorizations (especially to classification problems in anthropology, ethnology, archeology, linguistics but also in ontologies), but atypical instances of a concept cannot be apprehended adequately by different degrees from a prototype. Other formal approaches, as paraconsistent logics or non monotonic logics, conceptualize often atypical objects as exceptions. It had yet been developed an alternative way with the logics of determination of the objects (LDO). In this paper, we present the logics of typical and atypical (LTA) in order to give directly a logical approach of typicality / atypicality associated to a concept by a more common way than in LDO, in using only classes and not determination operators. It is introduced a distinction between predicative property and concept defined with its intension and its essence, a part of intension. A typical instance of a concept inherits all properties of intension; a typical instance inherits only properties of essence but it is a full member of the category associated to a concept and not a member with a weak degree of membership. In natural categorization, there are often instances (the exceptions) which do not inherit some properties of the essence; they cannot be considered as atypical instance and belong to the boundary of the category.

This paper addresses Trace-Based Reasoning (TBR) by using Case-Based Reasoning (CBR) as a descriptive framework. TBR is a reasoning paradigm in which inferences are made on specific objects called traces. Traces are sequential records of events observed and stored during an interactive process. We report two contributions. First, we propose a review of the current researches related to TBR. Then, we compare CBR and TBR. From this comparison, we show that the exploitation of traces instead of cases as knowledge sources raises very specific challenges. More precisely, new methods for defining similarity measures and for performing adaptation of traces are required. These new methods have to take into account the sequential properties of traces. We emphasis the benefits of using traces as a knowledge container in a reasoning process and we pinpoint promising applications of TBR.

This paper presents an efficient adaptation and application of the Dempster-Shafer theory of evidence, one that can be used effectively in a massively parallel hierarchical system for visual pattern perception. It describes the techniques used, and shows in an extended example how they serve to improve the system's performance as it applies a multiple-level set of processes.

An evidential reasoning mechanism based on the Dempster-Shafer theory of evidence is introduced. Its performance in real-world image analysis is compared with other mechanisms based on the Bayesian formalism and a simple weight combination method.

We consider the relation between knowledge and certainty, where a fact is known if it is true at all worlds an agent considers possible and is certain if it holds with probability 1. We identify certainty with probabilistic belief. We show that if we assume one fixed probability assignment, then the logic KD45, which has been identified as perhaps the most appropriate for belief, provides a complete axiomatization for reasoning about certainty. Just as an agent may believe a fact although phi is false, he may be certain that a fact phi, is true although phi is false. However, it is easy to see that an agent can have such false (probabilistic) beliefs only at a set of worlds of probability 0. If we restrict attention to structures where all worlds have positive probability, then S5 provides a complete axiomatization. If we consider a more general setting, where there might be a different probability assignment at each world, then by placing appropriate conditions on the support of the probability function (the set of worlds which have non-zero probability), we can capture many other well-known modal logics, such as T and S4. Finally, we consider which axioms characterize structures satisfying Miller's principle.