Context-aware computing is based on using knowledge about the current context. Interpretation of current context to an understandable knowledge is carried out by reasoning over context and in some cases by matching the current context with the desired context. In this thesis we concentrated on context matching issue in context-aware computing domain. Context matching can be done in various ways like it is done in other matching processes. Our matching approach is best matching in order to generate granular similarity results and not to be limited to Boolean values. We decided to use Ontology as the encoded domain knowledge for our matching method. Context matching method is related to the method that we represent context. We selected conceptual graphs to represent the context. We proposed a generic algorithm for context matching based on the ontological information that benefit from the conceptual graph theory and its advantages.

Intelligent devices are quickly becoming necessities to support our activities during both work and play. We are already bound in a symbiotic relationship with these devices. An unfortunate effect of the pervasiveness of intelligent devices is the substantial investment of our time and effort to communicate intent. Even though our increasing reliance on these intelligent devices is inevitable, the limits of conventional methods for devices to perceive human expression hinders communication efficiency. These constraints restrict the usefulness of intelligent devices to support our activities. Our communication time and effort must be minimized to leverage the benefits of intelligent devices and seamlessly integrate them into society. Minimizing the time and effort needed to communicate our intent will allow us to concentrate on tasks in which we excel, including creative thought and problem solving. <br /><br /> An intuitive method to minimize human communication effort with intelligent devices is to take advantage of our existing interpersonal communication experience. Recent advances in speech, hand gesture, and facial expression recognition provide alternate viable modes of communication that are more natural than conventional tactile interfaces. Use of natural human communication eliminates the need to adapt and invest time and effort using less intuitive techniques required for traditional keyboard and mouse based interfaces. <br /><br /> Although the state of the art in natural but isolated modes of communication achieves impressive results, significant hurdles must be conquered before communication with devices in our daily lives will feel natural and effortless. Research has shown that combining information between multiple noise-prone modalities improves accuracy. Leveraging this complementary and redundant content will improve communication robustness and relax current unimodal limitations. <br /><br /> This research presents and evaluates a novel multimodal framework to help reduce the total human effort and time required to communicate with intelligent devices. This reduction is realized by determining human intent using a knowledge-based architecture that combines and leverages conflicting information available across multiple natural communication modes and modalities. The effectiveness of this approach is demonstrated using dynamic hand gestures and simple facial expressions characterizing basic emotions. It is important to note that the framework is not restricted to these two forms of communication. The framework presented in this research provides the flexibility necessary to include additional or alternate modalities and channels of information in future research, including improving the robustness of speech understanding. <br /><br /> The primary contributions of this research include the leveraging of conflicts in a closed-loop multimodal framework, explicit use of uncertainty in knowledge representation and reasoning across multiple modalities, and a flexible approach for leveraging domain specific knowledge to help understand multimodal human expression. Experiments using a manually defined knowledge base demonstrate an improved average accuracy of individual concepts and an improved average accuracy of overall intents when leveraging conflicts as compared to an open-loop approach.

Intelligent devices are quickly becoming necessities to support our activities during both work and play. We are already bound in a symbiotic relationship with these devices. An unfortunate effect of the pervasiveness of intelligent devices is the substantial investment of our time and effort to communicate intent. Even though our increasing reliance on these intelligent devices is inevitable, the limits of conventional methods for devices to perceive human expression hinders communication efficiency. These constraints restrict the usefulness of intelligent devices to support our activities. Our communication time and effort must be minimized to leverage the benefits of intelligent devices and seamlessly integrate them into society. Minimizing the time and effort needed to communicate our intent will allow us to concentrate on tasks in which we excel, including creative thought and problem solving. <br /><br /> An intuitive method to minimize human communication effort with intelligent devices is to take advantage of our existing interpersonal communication experience. Recent advances in speech, hand gesture, and facial expression recognition provide alternate viable modes of communication that are more natural than conventional tactile interfaces. Use of natural human communication eliminates the need to adapt and invest time and effort using less intuitive techniques required for traditional keyboard and mouse based interfaces. <br /><br /> Although the state of the art in natural but isolated modes of communication achieves impressive results, significant hurdles must be conquered before communication with devices in our daily lives will feel natural and effortless. Research has shown that combining information between multiple noise-prone modalities improves accuracy. Leveraging this complementary and redundant content will improve communication robustness and relax current unimodal limitations. <br /><br /> This research presents and evaluates a novel multimodal framework to help reduce the total human effort and time required to communicate with intelligent devices. This reduction is realized by determining human intent using a knowledge-based architecture that combines and leverages conflicting information available across multiple natural communication modes and modalities. The effectiveness of this approach is demonstrated using dynamic hand gestures and simple facial expressions characterizing basic emotions. It is important to note that the framework is not restricted to these two forms of communication. The framework presented in this research provides the flexibility necessary to include additional or alternate modalities and channels of information in future research, including improving the robustness of speech understanding. <br /><br /> The primary contributions of this research include the leveraging of conflicts in a closed-loop multimodal framework, explicit use of uncertainty in knowledge representation and reasoning across multiple modalities, and a flexible approach for leveraging domain specific knowledge to help understand multimodal human expression. Experiments using a manually defined knowledge base demonstrate an improved average accuracy of individual concepts and an improved average accuracy of overall intents when leveraging conflicts as compared to an open-loop approach.

In the master's thesis we are concerned with the logical perspective on business process model-ing. The logical perspective on business process modeling has several advantages. First, being a formal logical system, first-order logic let us thoroughly understand the foundations of process modeling. Second, after we understand the logical foundations of business process modeling, we are free to build a BPM language based entirely on logic, or map an existing language onto logic, which may be useful for artificial reasoning. Third, if the business process model is mapped to logic (or another declarative language) it can be easily stored in a declarative knowledge base. Forth, logic based process models can be used in companies as a basis for knowledge manage-ment. And fifth, the science of logic offers a number of various semantic enhancements, which can be used in favor of better business process modeling expressiveness. The first objective of the thesis is to perform a thorough review of the literature of both our fields -- the business process modeling and temporal logic. The related second objective is to study the ability of logic to represent processes and the notion of time in general, and to offer techniques for logical process representation. Subsequently, the examples should be provided in order to present that the selected techniques are capable of performing what is sketched in the first paragraph. The third objective is to propose improvements of the current business process modeling approach and provide relevant examples. Eventually, means of extending the tech-niques presented can be proposed, too. The major contribution of the thesis is that it constitutes a reasonable basis for further research in the chosen field. For novices or even experienced in the subject it represents a good stepping stone.

(in English): Conceptual graphs are a formal knowledge representation language introduced by John F. Sowa, an American specialist on Artificial Intelligence, at the end of the seventies. They are the synthesis of heuristic and formalistic approach to Artificial Intelligence and knowledge procession. They provide meaning and knowledge in form, which is logically precise, human- readable and untestable, and it is applicable in the computing domain in general. Conceptual graphs can be expressed through a first-order logic, which makes them a quality tool for intelligent reasoning. Their notation CGIF was standardised by norm ISO/IEC 24707:2007 as one of the three dialects of Common logic, which frames the set of logic based on logic. Conceptual graphs are also mappable to knowledge representation languages standardised for the Semantic Web; OWL and RDF (S). This work introduces the conceptual graph theory in the context of scientific fields like linguistics, logic and artificial intelligence. It represents the formalism proposed by John F. Sowa and some extensions that have emerged over the past decades, along with the need for improvements to the representational properties of graphs. Finally, the work provides an illustrative overview of the implementation and use of conceptual graphs in practice....