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Artificial intelligence (AI) methods have the potential for broad impact in smart homes. Different AI methods offer different contributions for this domain, with different design goals, tasks, and circumstances dictating where each type of method best applies. In this chapter, we describe motivations and opportunities for applying case-based reasoning (CBR) to a human-centered approach to the capture, sharing, and revision of knowledge for smart homes. Starting from the CBR cognitive model of reasoning and learning, we illustrate how CBR could provide useful capabilities for problem detection and response, provide a basis for personalization and learning, and provide a paradigm for home-human communication to cooperatively guide performance improvement. After sketching how these capabilities could be served by case-based reasoning, we discuss some design issues for applying CBR within smart homes and case-based reasoning research challenges for realizing the vision. [ABSTRACT FROM AUTHOR]

A new computing era after Mainframes, PC's and mobiles is becoming closer to reality since the beginning of the 21st century. This new era is often described with different terms such as pervasive, ubiquitous, ambient or context-aware computing. However, there is a common characteristic behind all these projections: They are all based on a substantially more flexible system understanding, whereby the thought of the system as a tool moves into the background and the needs and desires of the user step into the foreground. Such concepts for software applications being aware of their context are in fact not new, but become more and more important for productive fields of software and systems engineering and particularly in ubiquitous and wearable computing. In this paper we describe a generic mechanism for designing context awareness and adaptation behavior with formal methods, thus basically allowing an engineering approach in designing and implementing complex context aware adaptive systems while avoiding their usual pitfalls. [ABSTRACT FROM AUTHOR]

The paper presents a contexts recognition algorithm that uses the Internet as a knowledge base to extract the multiple contexts of a given situation, based on the streaming in text format of information representing the situation. Context is represented here as any descriptor most commonly selected by a set of subjects to describe a given situation. Multiple contexts are matched with the situation. The algorithm yields consistently good results and the comparison of the algorithm results with the results of people showed that there was no significant difference in the determination of context. The algorithm is currently being implemented in different fields and in multilingual environments. Keywords: Matching context, Context recognition, Metadata, Text analysis. [ABSTRACT FROM AUTHOR]

The easy creation of context-aware services requires the support of management facilities that provide ways to more easily acquire, represent and distribute context information. This paper claims that the quality level of a context-aware service determines the context information to be obtained. On the other hand, using context data produced by unsteady sources may affect the users' satisfaction. In this perspective, we introduce the Context Matching Engine that trades off the cost, the user preferences and the quality of the available context information in order to discover the best context sources for each customized context-aware service. According to the proposed approach, there is no need for the services to know beforehand the context providers to retrieve information, but the evaluation and the quality-aware selection of the context information on context request are envisioned. Finally, it allows services to be ported easily to environments with different set of context sources. [ABSTRACT FROM AUTHOR]

Current research in context-awareness is biased toward low-level context information. High-level context information, however, poses several challenges to context management systems, which can be traced back to the asynchronicity of context acquisition and use and the inherent dynamics and imperfection in that process. This paper presents a three layer model allowing for dealing with the problems of imperfection and aging in a controlled way. It conceives the problem of high-level user context management as an information management problem with specific requirements. The approach has been applied to a context-aware learning environment for corporate learning support. [ABSTRACT FROM AUTHOR]

Applications need facilities for recognizing and adapting to context in order to provide useful and user-centered results. There are several problems to be addressed when building context-aware applications, two of which being how to defineandmanage all available contextual information and how to distinguishrelevant from non-relevant context for a given task. In this paper, we focus on the second problem and propose a context definition and model for a context-aware agent. We exploit this model to build agents that learn to select relevant context and to use it to make decisions. [ABSTRACT FROM AUTHOR]

This paper proposes a component-based architecture and development methodology for context-aware systems. A context is formally defined from relational point of view. The architecture of a context-aware system is conceived as a composition of the two components context constructor and context adapter. To process dynamically changing contextual information, we introduce context calculus as the formal basis of context manipulation. The information and its sources are abstracted within this formal definition. As an illustration of the principles involved in developing a context-aware system, we discuss the Anti-lock Braking System problem. Keywords: context, context-awareness, co-design, components. [ABSTRACT FROM AUTHOR]

We present a service to model situations and retrieve contextual information in mobile and ubiquitous computing environments. We introduce the general user model and context ontology Gumo for the uniform interpretation of distributed situational information in intelligent semantic web enriched environments. Furthermore, we present the relation to the user model and context markup language UserML, that is used to exchange partial models between different adaptive applications. Our modeling and retrieval approach bases on semantic web technology and complex conflict resolution concepts. Keywords: situation modeling, smart context retrieval, user model and context ontology, semantic web, distributed situation service, user model markup language. [ABSTRACT FROM AUTHOR]

In the research project EPOSThis work has been supported by a grant from The Federal Ministry of Education, Science, Research, and Technology (FKZ ITW-01 IW C01). we build a pro-active, context-sensitive support system to aid the user with his knowledge work, which is mostly about searching, reading, creating, and archiving of documents. In order to avoid distracting the user, the context gathering is realized by installable user observation plugins for standard applications such as Mozilla Firefox and Thunderbird. The main part of this paper is about the definition of a context model for the personal knowledge management domain. The context model incorporates only contextual elements relevant to satisfy the knowledge worker's potential information need. It stores only information items known to the user (such as links to his own documents, folders, ), as well as, shared ontologies to assure an understanding of the context. The context is modeled in and can be retrieved by context-aware applications from the context support system via an XML-RPC call. [ABSTRACT FROM AUTHOR]

One of the cornerstones of any intelligent entity is the ability to understand how occurrences in the surrounding world influence its own behaviour. Different states, or situations, in its environment should be taken into account when reasoning or acting. When dealing with different situations, context is the key element used to infer possible actions and information needs. The activities of the perceiving agent and other entities are arguably one of the most important features of a situation; this is equally true whether the agent is artificial or not. This work proposes the use of Activity Theory to first model context and further on populate the model for assessing situations in a pervasive computing environment. Through the socio-technical perspective given by Activity Theory, the knowledge intensive context model, utilised in our ambient intelligent system, is designed. [ABSTRACT FROM AUTHOR]

R-GMA (Relational Grid Monitoring Architecture) [1] is a grid monitoring and information system that provides a global view of data distributed across a grid system. R-GMA creates the impression of a single centralised repository of information, but in reality the information can be stored at many different locations on the grid. The Registry and Schema are key components of R-GMA. The Registry matches queries for information to data sources that provide the appropriate information. The Schema defines the tables that can be queried. Without the combined availability of these components, R-GMA ceases to operate as a useful service. This paper presents an overview of R-GMA and describes the Registry replication design and implementation. A replication algorithm for the Schema has also been designed. [ABSTRACT FROM AUTHOR]

In ubiquitous computing, the user will be interrupted in performing a task only if the information is relevant to the task or highly important in the situation to justify the interrupt. The information selection and presentation therefore should be adapted to the user and his current context of use. Nowadays, uncounted Content-Management Systems provide access to a large amount of information, but without context, information is just data. This paper introduces Context-Management as a new approach for the design of context-aware systems in ubiquitous computing. As a proof of concept we illustrate three prototypical implementations of contextualized information systems in different applications domains and decompose the underlying framework into its foundational components. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze how people build an internal representation of actions done by others by matching observable data (objects, events, spatial and temporal context) with knowledge and by making inferences about not observable (intention, goal, anticipation, and planning) data. For that purpose, we list the set of dimensions that define "what an action is" accompanied with their contextual aspects, and the heuristics used by the human cognitive system. Finally, we propose that processing objects properties in terms of function-to-patient relation between objects, and in terms of patient-to-function within objects, is a possible way to explain the context effects with respect to the fine-grained vs. the large-grained semantic organization of the spatial and temporal context. [ABSTRACT FROM AUTHOR]

Context is the only means to identify the sense of a polysemous word. All algorithms for word sense disambiguation make use of information within a context window of the target word. What is the best window size for word sense disambiguation has been long a problem. Different contexts generally give different results even for a same algorithm. In this paper, we exploit an algorithm which is more robust with the varying of different context used. This method aims to lower the uncertainty brought by classifiers using different context window sizes and make more robust utilization of context while perform well. Experiments show our approach outperforms some other algorithms on both robustness and performance. [ABSTRACT FROM AUTHOR]

Theories of context in logic enable reasoning and deduction in contexts represented as formal objects. Such theories are not readily applicable to systems that learn by induction from a set of examples. Probabilistic graphical models already provide the tools to exploit context represented as statistical dependences, thereby providing a unified methodology to incorporate context information in learning and inference. Drawing on a case study from optical character recognition, we present the various types of dependences that can occur in pattern classification problems and how such dependences can be exploited to increase classification accuracy. Learning under different conditions require differing amounts and kinds of samples and different trade-offs between modeling error due to overly strict independence assumptions and estimation error of models that are too elaborate for the size of the available training set. With a series of examples based on frames of two patterns we show how each kind of dependence can be represented using graphical models and present examples from other disciplines where the particular dependence frequently occurs. [ABSTRACT FROM AUTHOR]

This paper motivates and presents a nonmonotonic version of Contextual Intensional Logic, a type-theoretic logic intended as a general formalism for reasoning about context. In developing this logic, it is necessary to think through interactions between nonmonotonic and intensional logic that are interesting in their own right. The paper concludes with an extended example how nonmonotonic lifting rules can be deployed in inter-contextual reasoning. [ABSTRACT FROM AUTHOR]

A concept for performing threat analysis is developed. The goal is to establish a systematic approach for predicting, detecting and characterizing threat activity; allowing automation of some of these functions. The proposed approach explicitly addresses the fundamental problems of (a) sparse and ambiguous indicators of potential or actualized threat activity buried in massive background data; and (b) uncertainty in threat capabilities, intent and opportunities. Threats are modeled in terms of potential and actualized relationships between perpetrators (threatening entities) and targets (threatened entities). Threats may be intentional or unintentional (e.g. potential natural disasters or human error). Intentional threats can also have unintended consequences. Attack hypotheses are adaptively generated, evaluated and refined as the understanding of the situation evolves. This effort builds upon advances in Situation, Ontology and Estimation theory. [ABSTRACT FROM AUTHOR]

The paper presents a context-based approach to operational decision support. The approach focuses on modelling and solving a problem considering changes in the dynamic environment. The problem is modelled by abstract and operational contexts integrating information provided by information sources and domain knowledge. The approach involves ontology management operations for knowledge integration, context management techniques for information organisation, and object-oriented constraint network mechanisms for problem definition and solving. The approach is tested as an adaptive service for on-the-fly portable hospital configuration. [ABSTRACT FROM AUTHOR]

Collaborative learning can be motivated through environments that provide tools for communication. However, most available environments do not provide support neither for the students' reflection and self-regulation processes, nor to the teachers' activities. This support could be provided through the analysis of the interactions occurred within the CSCLE and stored in a group memory. The main weakness of the approaches up to now is to neglect the context where the interactions occur and the inadequate mechanism of the interactions' persistence. In this light, we propose the creation of a Learning Interaction Memory (LIM) to store the learning interactions. It will be modelled in a multidimensional structure and included in its model contextual elements to enrich and qualify the stored knowledge. By including contextual elements in the LIM, it will be possible to adapt dynamically its information to the concrete current situation, in order to provide more useful just-in-time feedback. [ABSTRACT FROM AUTHOR]

Spatial and temporal granularity can be understood as parameters of context restricting the set of accessible objects in a context. Starting from the idea that this selection process depends to a large extent on the relation between the grain-size of the context and the local extension of the objects, the granularity of a context is in this article formalised as a class of possible sizes in the context. This formalisation is shown to be in accordance to well-known mathematical foundations on perceptual classification. An example for the case of temporal granularity illustrates how the introduction of new elements into a context may result in a more or less smooth shifting of the granularity leading to a classification of four different types of change of granularity. The results can be applied in a wide range of fields, e.g. in research on contextual reasoning and natural language understanding. [ABSTRACT FROM AUTHOR]

Most direct reference theorists believe that ‘I' is a pure indexical. This means that when ‘I' is uttered, it contributes with the speaker to what is said. But, from some conceptions of metonymy as reference transfer, if ‘I' is used metonymically, it has an improper meaning and the object referred to is not the speaker. We will show that all theories of metonymy as transfer are inadequate and so they cannot determine if the metonymic use of ‘I' is a counterexample to its consideration as a pure indexical or not. We argue that the appropriate conception on metonymy is to consider it as a case of language reduction and that ‘I', when used metonymically, is just a part of a non-textual complete noun phrase; ‘I' has the semantic value that it usually has. The metonymic use of ‘I' does not risk the consideration of ‘I' as a pure indexical. [ABSTRACT FROM AUTHOR]

Multi-context systems can be used to represent contextual information and inter-contextual information flow. We show that the local model semantics of a multi-context system is completely determined by the information that is obtained when simulating the information flow specified by the system, in such a way that a minimal amount of information is deduced at each step of the simulation. The multi-context system framework implicitly presupposes that information flow is deterministic. In many natural situations, this is not a valid assumption. We propose an extension of the framework to account for non-determinism and provide an algorithm to efficiently compute the meaning of non-deterministic systems. [ABSTRACT FROM AUTHOR]

We describe a new point of view that uses contextual information of the interrelated conditions in which criminality or deviant occurs by means of computer simulation to examine its role in a progressively changing society. Based on the functionalist point of view, we model society as a multi agent system and criminality as a noise. A noise is injected as training signals to the single layer perceptrons that functions as agents (i.e. society) and all agents must comply the fitness function. Failure to comply will result in the agent being removed from the "society" and be replaced by a "newborn" that inherits some "upbringing" information from its "mother's agent". Our simulation studies point toward the constructive effects of criminality. The major contribution in our study is that new paradigm can show and measure criminality effects as in contrast to mere verbal descriptions of the social sciences. The new paradigm could be used to provide the functional explanation of atypical trends in criminality in East- and Middle European and some other countries and provide "artificial experimental data" for future studies. [ABSTRACT FROM AUTHOR]

Researchers in "contextual vocabulary acquisition" differ over the kinds of context involved in vocabulary learning, and the methods and benefits thereof. This paper presents a computational theory of contextual vocabulary acquisition, identifies the relevant notion of context, exhibits the assumptions behind some classic objections, and defends our theory against these objections. [ABSTRACT FROM AUTHOR]

While interacting with other users in dynamic use contexts, one often aims at coordinating activities as events unfold. Such coordination can often be unplanned or impromptu. There are opportunities for supporting impromptu coordination among mobile individuals by representing and processing contextual information. In this paper we present a novel technique, based on goal-oriented automated negotiation, to enable computational agents acting on behalf of users to automatically negotiate opportunities for coordination. Our focus is on the technology ‘under the skin' that can represent, analyse, and integrate information to support the user's tasks in a timely and appropriate way. An implemented prototype is demonstrated via a scenario, which is based on a workday narrative. [ABSTRACT FROM AUTHOR]

We present AKIRA, an agent-based hybrid architecture designed for cognitive modeling. We describe some of the underlying ideas motivating its development, such as the possibility of exploiting distributed representations and performing parallel, dynamic and context aware computation. We illustrate its main components and capabilities and compare it with some related cognitive architectures, such as DUAL and Copycat. We present also a sample simulation in the visual search domain, exploiting AKIRA's peculiarities for cognitive modeling. [ABSTRACT FROM AUTHOR]

We propose and develop an approach modeled with multi-attribute utility theory for sensor fusion in context-aware environments. Our approach is distinguished from existing general purpose fusion techniques by a number of factors including a general underlying context model it is built upon and a set of heuristics it covers. The technique is developed for context-aware applications and we argue that it provides various advantages for data fusion in context-aware scenarios. We experimentally evaluate our approach with actual use cases using real sensors. [ABSTRACT FROM AUTHOR]

In order to help address problems of information overload in digital imagery task domains, we have developed an interactive approach to the capture and reuse of image context information. Our framework models different aspects of the relationship between images and the domain tasks that they support by monitoring the interactive manipulation and annotation of task-relevant imagery. In particular, a strong focus on task context serves to ground image annotations in domain specific goals. This contrasts with prevalent annotation schemes that focus on what individual images contain but that provide no context for which, if any, of those aspects are important to users. Our work attempts to leverage a measure of the user's intentions with regard to tasks that they address. We analyze human-computer interaction information that enables us to infer why image contents are important in a particular context and how specific images have been used to address particular domain goals. [ABSTRACT FROM AUTHOR]

We test a method of clustering dialects of English according to patterns of shared phonological features. Previous linguistic research has generally considered phonological features as independent of each other, but context is important: rather than considering each phonological feature individually, we compare the patterns of shared features, or Mutual Information (MI). The dependence of one phonological feature on the others is quantified and exploited. The results of this method of categorizing 59 dialect varieties by 168 binary internal (pronunciation) features are compared to traditional groupings based on external features (e.g., ethnic, geographic). The MI and size of the groups are calculated for taxonomies at various levels of granularity and these groups are compared to other analyses of geographic and ethnic distribution. Applications that could be improved by using MI methods are suggested. [ABSTRACT FROM AUTHOR]

Belief, defined as a knowledge structure with a degree of subjective confidence that is gained through interaction with other agents in a community, can play a crucial role in cognitive processes. As paradigmatic applications of our socially supported knowledge-belief system, (a) a utterance understanding parser was designed and implemented in Common Lisp CLOS with a belief management system, and (b) a cognitive model of attachment is proposed based on the analysis of analyzing fan letters sent to a toy company. [ABSTRACT FROM AUTHOR]

A rather common way of formalizing contexts as first class objects starts from the basic relation ist(c,p) which asserts that the proposition p is true in the context c. However, the space in which terms take values may itself be context-sensitive. Our aim is to introduce contexts as abstract mathematical entities in a more general framework which includes context-sensitivity, namely knowledge represented by contextual information systems. Making use of some concepts from the Rough Set Theory we refine two relations: the indiscernibility relation between the objects and the similarity relation between the contexts within a contextual information system. Both relations are illustrated with examples showing how contextual information systems can express in a natural way a very few well known phenomena. Based on these relations we propose a simple strategy for decreasing the ambiguity of contextual information systems. [ABSTRACT FROM AUTHOR]

Networks have been a common way to show pathways for supporting communities. Usually the local focus of the actors in these networks does not allow them to perceive the broader context of their current interest. In this paper the authors propose an ontology-based approach to link people who have no explicit relation in the network despite potential common interests. With respect to the results we extend the concept of integrating ontologies into social networks to shared information spaces. [ABSTRACT FROM AUTHOR]

Although being a daily task, the search for a word among others words is a new research domain we investigated in order to find the kinds contextual factors that can facilitate semantic oriented visual search. We report two experiments assessing task context, visual context and semantic context. Some of our results are found to be those of classical non-semantic visual search, while others show the impact of the semantic context. Basic recommendations can be find out for Human-Computer conception and cognitive chronometry methodology. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive resource self-management system that collects system resources, user information, and usage patterns as context information for utilization in self-configuration. This system will ease the system maintenance burden on users by automating a large portion of the configuration tasks such as; install, reconfiguration and update, while also decreasing cost and errors. Working from the gathered context information, this system allows users to select and install appropriate components for their system context. This also offers a more personalized configuration setting by using user's existing application setting and usage pattern. To avoid a center server overload when transferring and managing related files, we employ Peerto- Peer method. A prototype was developed to evaluate the system with a comparison study using the conventional methods of manual configuration and MS-IBM systems was conducted to validate the proposed system in terms of functional capacity, install time, etc... [ABSTRACT FROM AUTHOR]

Proactive retrieval systems monitor a user's task context and automatically provide the user with related resources. The effectiveness of such systems depends on their ability to perform context-based retrieval, generating queries which return context-relevant results. Two factors make this task especially challenging for Web-based retrieval. First, the quality of Web retrieval can be strongly affected by the vocabulary used to generate the queries. If the system's vocabulary for describing the context differs from the vocabulary used in the resources themselves, relevant resources may be missed. Second, search engine restrictions on query length may make it difficult to include sufficient contextual information in a single query. This paper presents an algorithm, IACS (Incremental Algorithm for Context-Based Search), which addresses these problems by building up, applying, and refining partial context descriptions incrementally. In IACS, an initial term-based context description is the starting point for a cycle of mining search engines, performing context-based filtering of results, and refining context descriptions to generate new rounds of queries in an expanded vocabulary. IACS has been applied in a system for proactively supporting concept-map-based knowledge modeling, by retrieving resources relevant to target concepts in the context of the rich information provided by "in progress" concept maps. An evaluation of the system shows that it provides significant improvements over a baseline for retrieving context-relevant resources. We expect the algorithm to have broad applicability to context-based Web retrieval for rich contexts. [ABSTRACT FROM AUTHOR]

The way we see the objects around us determines speech and gestures we use to refer to them. The gestures we produce structure our visual perception. The words we use have an influence on the way we see. In this manner, visual perception, language and gesture present multiple interactions between each other. The problem is global and has to be tackled as a whole in order to understand the complexity of reference phenomena and to deduce a formal model. This model may be useful for any kind of man-machine dialogue system that focuses on deep comprehension. We show how a referring act takes place into a contextual subset of objects, called ‘reference domain,' and we present the ‘multimodal reference domain' model that can be exploited in a dialogue system when interpreting. [ABSTRACT FROM AUTHOR]

In this paper we present an Embodied Conversational Agent (ECA) model able to display rich verbal and non-verbal behaviors. The selection of these behaviors should depend not only on factors related to her individuality such as her culture, her social and professional role, her personality, but also on a set of contextual variables (such as her interlocutor, the social conversation setting), and other dynamic variables (belief, goal, emotion). We describe the representation scheme and the computational model of behavior expressivity of the Expressive Agent System that we have developed. We explain how the multi-level annotation of a corpus of emotionally rich TV video interviews can provide context-dependent knowledge as input for the specification of the ECA (e.g. which contextual cues and levels of representation are required for enabling the proper recognition of the emotions). [ABSTRACT FROM AUTHOR]

Tourists who don't select standard guided tours have to rely on a map or on signs to explore an unknown city on their own. They often end up looking for information about an attraction they're at - but there's none available. They need a guidance leading them wherever they want, giving them information about whatever they find and furthermore care for their return in time. This is the main objective of the Dynamic Tour Guide (DTG). The DTG is a mobile agent enabling a personalized spontaneous guided tour. It selects attractions, plans an individual tour, provides navigational guidance during the execution and offers environmental information. This kind of ambient intelligence is based on the analysis of all available contextual information to support the tourist in any possible way with the help of mobile devices. [ABSTRACT FROM AUTHOR]

As the number of mobile devices we carry grows, the job of managing those devices throughout the day becomes cumbersome. This is especially true for cell phones. Despite the many benefits they provide, cell phones create problems that arise from a mismatch between the user's context and the cell phone's behavior. In large part, the mismatch occurs because owners do not remember to frequently update their cell phone configuration according to the current context. It is desirable for mobile devices to automatically configure themselves based on the context of the environment and user preferences. Given the personal attachment between people and their mobile devices such as cell phones, context-aware automatic configuration may not be the preferred solution for users. We have conducted an in situ experiment to examine the feasibility, effectiveness and people's reactions to such a solution. Our results show that people prefer automatic configuration over configuring their devices by hand, and they are willing to adopt it in real life. The results also suggest that a hybrid, passive-active, context-aware configuration approach is preferred over a purely passive or active one. [ABSTRACT FROM AUTHOR]

Context effects during Prisoner's Dilemma (PD) game playing are investigated. The Cooperation Index (CI) - a quantity computed as a relation between payoffs - defines a cooperativeness scale, along which PD games can be distributed. Context is selected by manipulating the CI range of the games played. It is found that the level of cooperation depends not only on the CI of the current game but also on the CI of the other games in the sequence. The influence of context on the full CI scale is investigated by introducing probe games covering the whole CI range. The results are compared with the prediction of a model that takes into account the current game, the previous play history, and the predicted opponent's move. The model is found to be sensitive to both the CI and to the CI of the other games form the game set and a very good agreement between the model and the experimental data was found. [ABSTRACT FROM AUTHOR]

Contextual information plays a key role in the automatic interpretation of text. This paper is concerned with the identification of textual contexts. A context taxonomy is introduced first, followed by an algorithm for detecting context boundaries. Experiments on the detection of subjective contexts using a machine learning model were performed using a set of syntactic features. [ABSTRACT FROM AUTHOR]

The K-SCM is an unsupervised learning algorithm, designed to cluster symbol string data in an on-line manner. Unlike many other learning algorithms there are no time dependent gain factors. Context recognition based on the fusion of information sources is formulated as the clustering of symbol string data. Applied to real measured context data it is shown how the clusters can be associated with higher level contexts. This unsupervised learning approach is fundamentally different to the approach based, for example, on ontologies or supervised learning. Unsupervised learning requires no intervention from an outside expert. Using the example of menu adaptation in a mobile device, and a second learning stage, it is shown how user requirements in a given context can be associated with the learned contexts. This approach can be used to facilitate user interaction with the device. [ABSTRACT FROM AUTHOR]

In light of recent attempts to design context-aware mobile phones, this paper contributes by providing findings from a study of mobile phone talk in context. We argue the benefits of investigating empirically the ways in which a place is interactionally constituted as appropriate, or not, for a mobile phone conversation. Based on a study of naturally occurring mobile phone talk, we show how people handle calls in potentially difficult situations. Availability is negotiated, and it is not always agreed on whether a situation is appropriate. These findings pose challenges to the design of context-aware telephony. [ABSTRACT FROM AUTHOR]

When linguists or philosophers of language study communication, they are naturally biased towards linguistic communication. This has resulted in a situation in which very little attention is being paid to the fact that many of our daily utterances are actually a mixture between linguistic and other expressive means, such as noises, gestures and facial expressions. It is precisely on this phenomenon that I wish to focus. I show how Clark & Gerrig's notion of demonstration can be usefully applied to intrusions of non-linguistic material into spoken or written utterances. Although I agree with relevance theorists that the linguistic bias should be abandoned, I am led to propose an account of non-linguistic demonstrations as linguistic constituents, at least in the particular category of data that I examine, namely utterances in which some gesturing appears to stand in for a missing linguistic constituent. In those cases, I contend, non-linguistic demonstrations are recruited to fulfil various syntactic functions. I justify this somewhat paradoxical stance by exploring the essential similarities between quotations and non-linguistic demonstrations. [ABSTRACT FROM AUTHOR]

Much context data comes from mobile, transient, and unreliable sources. Such resources are best specified by descriptive names identifying what data is needed rather than which source is to provide it. The design of descriptive names has important consequences, but until now little attention has been focused on this problem. We propose a descriptive naming system for providers of context data that provides more flexibility and power than previous naming systems by classifying data providers into "provider kinds" that are organized in an evolving hierarchy of subkinds and superkinds. New provider kinds can be inserted in the hierarchy not only as subkinds, but also as superkinds, of existing provider kinds. Our names can specify arbitrary boolean combinations of arbitrary tests on data-source attributes, yielding expressive power not found in naming schemes based on attribute matching. [ABSTRACT FROM AUTHOR]

A model for context-dependent natural language semantics is proposed and formalized in terms of possible worlds. The meaning of a sentence depends on context and at the same time affects that context representing the knowledge about the world collected from a discourse. The model fits well with a "flat" semantic representation as first proposed by Hobbs (1985), consisting basically of a conjunction of atomic predications in which all variables are existentially quantified with the widest possible scope; in our framework, this provides very concise semantic terms as compared with other representations. There is a natural correspondence between the possible worlds semantics and a constraint solver, and it is shown how such a semantics can be defined using the programming language of Constraint Handling Rules (Frühwirth, 1995). Discourse analysis is clearly a process of abduction in this framework, and it is shown that the mentioned constraint solvers serve as effective and efficient abductive engines for the purpose. [ABSTRACT FROM AUTHOR]

In this paper, we describe an exploratory study to develop a model of visual attention that could aid automatic interpretation of exophors in situated dialog. The model is intended to support the reference resolution needs of embodied conversational agents, such as graphical avatars and robotic collaborators. The model tracks the attentional state of one dialog participant as it is represented by his visual input stream, taking into account the recency, exposure time, and visual distinctness of each viewed item. The model correctly predicts the correct referent of 52% of referring expressions produced by speakers in human-human dialog while they were collaborating on a task in a virtual world. This accuracy is comparable with reference resolution based on calculating linguistic salience for the same data. [ABSTRACT FROM AUTHOR]

Object identification via a perceptual-demonstrative mode of presentation has been studied in cognitive science as a particularly direct and context-dependent means of identifying objects. Several recent works in cognitive science have attempted to clarify the relation between attention, demonstrative identification and context exploration. Assuming a distinction between ‘(language-based) demonstrative reference' and ‘perceptual demonstrative identification', this article aims at specifying the role of attention in the latter and in the linking of conceptual and non conceptual contents while exploring a spatial context. First, the analysis presents an argument to the effect that selection by overt and covert attention is needed for perceptual demonstrative identification since overt/covert selective attention is required for the situated cognitive access to the target object. Second, it describes a hypothesis that makes explicit some of the roles of attention: the hypothesis of identification by epistemic attention via the control of perceptual routines. [ABSTRACT FROM AUTHOR]

Enterprises develop procedures to address focuses in any case. However, procedures result often in sub-optimal solutions for any specific focus. As a consequence, each actor develops his own practice to address a focus in a given context, focus and its context being particular and specific. The modeling of practices is not an easy task because there are as many practices as contexts of occurrence. This paper proposes a way to deal practically with practices. Based on our definition of context, we present a context-based representation formalism for modeling task accomplishment by users called contextual graphs and its interest for the tasks of incremental acquisition, learning and explanation. Contextual graphs are discussed on a modeling in information retrieval. [ABSTRACT FROM AUTHOR]

According to epistemological contextualism, a sentence of the form "S knows that p" doesn't express a complete proposition. Different utterances of the sentence, in different contexts, can express different propositions: "know" is context-dependent. This paper deals with the semantic contextualist thesis grounding epistemological contextualism. We examine various kinds of linguistic context dependence, which could be relevant to epistemological contextualism: ambiguity, ellipsis, indexicality, context-sensitivity of scalar predicates, dependence on standards of precision. We argue that only an accurate analysis of the different varieties of context sensitivity secures us a better understanding and a clearer evaluation of the contextualist approach. [ABSTRACT FROM AUTHOR]