It is widely agreed on that most cognitive processes are contextual in the sense that they depend on the environment, or context, inside which they are carried on. Even concentrating on the issue of contextuality in reasoning, many different notions of context can be found in the Artificial Intelligence literature. Our intuition is that reasoning is usually performed on a subset of the global knowledge base. The notion of context is used as a means of formalizing this idea of localization. Roughly speaking, we take a context to be the set of facts used locally to prove a given goal plus the inference routines used to reason about them (which in general are different for different sets of facts). Our perspective is similar to that proposed in [McC87, McC91]. The goal of this paper is to propose an epistemologically adequate theory of reasoning with contexts. The emphasis is on motivations and intuitions, rather than on technicalities. The two basic definitions are reported i...

Decision-support systems that help solving problems in open and weak theory domains, i.e. hard problems, need improved methods to ground their models in real world situations. Models that attempt to capture domain knowledge in terms of, e.g. rules or deeper relational networks, tend either to become too abstract to be efficient, or too brittle to handle new problems. In our research we study how the incorporation of case-specific, episodic, knowledge enables such systems to become more robust and to adapt to a changing environment by continuously retaining new problem solving cases as they occur during normal system operation. The research reported in this paper describes an extension that incorporates additional knowledge of the problem solving context into the architecture. The components of this context model is described, and related to the roles the components play in an abductive diagnostic process. Background studies are summarized, the context model is explained, and an example shows its integration into an existing knowledge-intensive CBR system.

Context effects on problem solving demonstrated so far in the literature are the result of systematic manipulation of some supposedly irrelevant to the solution elements of the problem description. Little attention has been paid to the role of casual entities in the environment which are not part of the problem description, but which might influence the problem solving process. The main purpose of the current paper is to avoid this limitation and to study the context effects (if any) caused by such accidental elements from the problem solver's environment and in this way to test the predictions made by the dynamic theory of context and its implementation in the DUAL cognitive architecture. Two experiments have been performed. In Experiment I the entities whose influence is being tested are part of the illustrations accompanying the target problem descriptions and therefore they belong to the core of the context, while in Experiment II the tested entities are part of the illustrations a...

Introduction After a long and exhausting war between the representatives of the symbolic and connectionist approaches (this war stimulated, however, the clarification of the limitations and advantages of both approaches) a growing group of peace-makers emerged who tried to integrate the advantages of both approaches and to fill in the gap between them (Hendler, 1989a, Hinton, 1990, Barnden & Pollack, 1991, Thornton, 1991, Sun & Bookman, 1992, 1994, Dinsmore, 1992, Holyoak & Barnden, 1994). However, a mini-war started between the peace-makers themselves on the issue how to sign the peace treaty: with the surrender of one of the approaches or with their parity. Some researchers supported the connectionist-to-the-top view that symbol structures and symbol processing should emerge from the work of a neural network (called a unified approach in chapters 2 and 4 of this volume and connectionist symbol processing in (Pollack, 1990, Smolensky, 1990, Touretzky, 1990, Smolensky et al.,

In conventional securC y systems, prtems,C rtems,C such as documents,har. ar devices and softwar applications follow an On/O# access policy. On, allows togr. t access and o#for denying access. This access policy ispr.flflTC5TA based on theuserT identity and is static over time. As applications becomemor per asive,securq y policies must becomemor flexible inor.A torC. ond to these highly dynamic computing envir/.fl. ts. That is,securfl yinfr'C5T.#T.Cr will need to be sensitive to context. Inor.A to meet theserseCTTqTq ts, wepr/ ose a conceptual modelfor context-basedauthorsedCT/q tuning. This model o#er afine-gr5.qq contrC over access onprTT#flTC rTT#flTC5. based on a set ofuser. and envir'qA' t state andinforxflC5.x 1

Currently, context-aware applications are dened as applications that react appropriately to information sensed in the environment, as opposed to applications that elaborate only information explicitly provided by users. Context is (implicitly or explicitly) thought of as a collection of features of the (physical or virtual) environment which can aect the behavior of an application. Though this notion of context is relatively unproblematic in systems with central control, it raises a number of challenging issues when applied to distributed systems, namely systems in which control is distributed over a group of heterogeneous, autonomous, interacting entities (typically, agents). Indeed, in distributed applications, we cannot assume that autonomous entities share a context, even though each of them uses contextual information for its operations. In this essay, we discuss in detail this claim and present a notion of context which seems to be adequate for distributed systems. For the sake of illustration, we outline how this notion of context can be used to design distributed context-aware systems. 1

This paper presents a computer simulation of context effects on problem solving with AMBR --- a model of human analogy-making. It demonstrates how perceiving some incidental objects from the environment may change the way the problem is being solved. It also shows that the timing of this perception is important: while the context element may have crucial influence during the initial stages of problem solving it has virtually no effect during the later stages. The simulation also explores the difference between an explicit hint condition where the focus of attention is drawn towards a context situation which is analogous to the target problem and an implicit context condition where an arbitrary object from the environment makes us remind an old episode.

In this paper we present a formal approach to modelling context effect in perceptual domains: namely how the perception of an object is effected by other objects. Our approach is operational and perceptually motivated in the sense that we focus on how objects are perceived as being constructed from certain components. Based on a psychological theory of perception, called Structural Information Theory, we develop an algebraic model for context sensitive perception. We illustrate our model by using the domain of alphabetic strings and discuss its extension to the domain of visual objects. Finally, we remark on how this approach can be applied to model context effect in non-perceptual situation, and then make some observations on the general problem of context.

Several distinctions between various concepts of context are discussed: internal vs. external, intrinsic vs. model-based, and implicit vs. explicit. Finally, a dynamic theory of implicit, intrinsic, internal context is briefly discussed and its application to a context-sensitive general cognitive architecture DUAL and a context-sensitive model of human reasoning, AMBR, are briefly outlined. Keywords: implicit context, intrinsic context, internal context, dynamic approach CONTEXT EFFECTS Psychologists have demonstrated context effects on virtually all cognitive processes. Thus, for example, context effects on perception have been demonstrated by Gestalt psychologists in various forms: different interpretations of ambiguous figures; visual illusions depending on the background elements or on the presence of other stimuli. In language comprehension context effects can be exemplified by lexical, syntax, semantic, inference, thematic and other types of context effects (Tiberghien, 19...

Traditionally, context-aware applications are dened as applications that react appropriately to information sensed in the environment, as opposed to applications that elaborate only information explicitly provided by users. Context