Online research in philosophy

We investigate the notion of relevance as it pertains to ‘commonsense’, subjunctive conditionals. Relevance is taken here as a relation between a property (such as having a broken wing) and a conditional (such as birds typically fly). Specifically, we explore a notion of ‘causative’ relevance, distinct from ‘evidential’ relevance found, for example, in probabilistic approaches. A series of postulates characterising a minimal, parsimonious concept of relevance is developed. Along the way we argue that no purely logical account of relevance (even at the metalevel) is possible. Finally, and with minimal restrictions, an explicit definition that agrees with the postulates is given.

We investigate the notion of relevance as it pertains to ‘commonsense’, subjunctive conditionals. Relevance is taken here as a relation between a property (such as having a broken wing) and a conditional (such as birds typically fly). Specifically, we explore a notion of ‘causative’ relevance, distinct from ‘evidential’ relevance found, for example, in probabilistic approaches. A series of postulates characterising a minimal, parsimonious concept of relevance is developed. Along the way we argue that no purely logical account of relevance (even at the metalevel) is possible. Finally, and with minimal restrictions, an explicit definition that agrees with the postulates is given.

In this paper we discuss the relevance of considering context for critical thinking. We argue that critical thinking is best viewed in terms of ‘critical inquiry’ in which argumentation is seen as a way of arriving at reasoned judgments on complex issues. This is a dialectical process involving the comparative weighing of a variety of contending positions and arguments. Using the model which we have developed for teaching critical thinking as critical inquiry, we demonstrate the role played by the following aspects of context: (1) knowledge of the dialectical context (the debate around an issue, both current and historical); (2) an understanding of the current state of practice and belief surrounding an issue; (3) an understanding of the intellectual, political, historical and social contexts in which an issue is embedded; (4) knowledge of the relevant disciplinary context; (5) information about the sources of an argument; (6) awareness of one’s own beliefs and biases.

Values are critical for intelligent behavior, since values determine interests, and interests determine relevance. Therefore we address relevance and its role in intelligent behavior in animals and machines. Animals avoid exhaustive enumeration of possibilities by focusing on relevant aspects of the environment, which emerge into the (cognitive) foreground, while suppressing irrelevant aspects, which submerge into the background. Nevertheless, the background is not invisible, and aspects of it can pop into the foreground if background processing deems them potentially relevant. Essential to these ideas are questions of how contexts are switched, which defines cognitive/behavioral episodes, and how new contexts are created, which allows the efficiency of foreground/background processing to be extended to new behaviors and cognitive domains. Next we consider mathematical characterizations of the foreground/background distinction, which we treat as a dynamic separation of the concrete space into (approximately) orthogonal subspaces, which are processed differently. Background processing is characterized by large receptive fields which project into a space of relatively low dimension to accomplish rough categorization of a novel stimulus and its approximate location. Such background processing is partly innate and partly learned, and we discuss possible correlational (Hebbian) learning mechanisms. Foreground processing is characterized by small receptive fields which project into a space of comparatively high dimension to accomplish precise categorization and localization of the stimuli relevant to the context. We also consider mathematical models of valences and affordances, which are an aspect of the foreground. Cells processing foregound information have no fixed meaning (i.e., their meaning is contextual), so it is necessary to explain how the processing accomplished by foreground neurons can be made relative to the context. Thus we consider the properties of several simple mathematical models of how the contextual representation controls foreground processing. We show how simple correlational processes accomplish the contextual separation of foreground from background on the basis of differential reinforcement. That is, these processes account for the contextual separation of the concrete space into disjoint subspaces corresponding to the foreground and background. Since an episode may comprise the activation of several contexts (at varying levels of activity) we consider models, suggested by quantum mechanics, of foreground processing in superposition. That is, the contextual state may be a weighted superposition of several pure contexts, with a corresponding superposition of the foreground representations and the processes operating on them. This leads us to a consideration of the nature and origin of contexts. Although some contexts are innate, many are learned. We discuss a mathematical model of contexts which allows a context to split into

Agents require a constant flow, and a high level of processing, of relevant semantic information, in order to interact successfully among themselves and with the environment in which they are embedded. Standard theories of information, however, are silent on the nature of epistemic relevance. In this paper, a subjectivist interpretation of epistemic relevance is developed and defended. It is based on a counterfactual and metatheoretical analysis of the degree of relevance of some semantic information i to an informee/agent a, as a function of the accuracy of i understood as an answer to a query q, given the probability that q might be asked by a. This interpretation of epistemic relevance vindicates a strongly semantic theory of information, according to which semantic information encapsulates truth. It accounts satisfactorily for several important applications and interpretations of the concept of relevant information in a variety of philosophical areas. And it interfaces successfully with current philosophical interpretations of causal and logical relevance.

The literature within library and information science (LIS) on relevance comes primarily from the subfields of information retrieval and information systems design. This literature has developed over time from an orthodoxy that has focused on relevance as an objective measure to a comprehension of the dynamic nature of relevance judgment. Other literatures, such as those of the philosophy of language and semantics, also have offered cogent thought that could and should be incorporated into LIS. This thought has broadened discussion to the context in which relevance is assessed, the speech acts that are evaluated, and the dialogic element of human communication.

The use of context can considerably facilitate reasoning by restricting the beliefs reasoned upon to those relevant and providing extra information specific to the context. Despite the use and formalization of context being extensively studied both in AI and ML, context has not been much utilized in agents. This may be because many agents are only applied in a single context, and so these aspects are implicit in their design, or it may be that the need to explicitly encode information about various contexts is onerous. An algorithm to learn the appropriate context along with knowledge relevant to that context gets around these difficulties and opens the way for the exploitation of context in agent design. The algorithm is described and the agents compared with agents that learn and apply knowledge in a generic way within an artificial stock market. The potential for context as a principled manner of closely integrating crisp reasoning and fuzzy learning is discussed.

Based on the premise that what is relevant, consistent, or true may change from context to context, a formal framework of relevance and context is proposed in which • contexts are mathematical entities • each context has its own language with relevant implication • the languages of distinct contexts are connected by embeddings • inter-context deduction is supported by bridge rules • databases are sets of formulae tagged with deductive histories and the contexts they belong to • abduction and revision are supported by a notion of consistency of formulae and sets of formulae which are relative to a context, and which can, in turn, be seen as constituents of agendas.

The issue of context arises in assorted areas of Artificial Intelligence. Although its importance is realized by various researchers, there is not much work towards a useful formalization. In this paper, we will present a preliminary model (based on Situation Theory) and give examples to show the use of context in various fields, and the advantages gained by the acceptance of our proposal.

At the heart of natural language processing is the understanding of context dependent meanings. This paper presents a preliminary model of formal contexts based on situation theory. It also gives a worked-out example to show the use of contexts in lifting, i.e., how propositions holding in a particular context transform when they are moved to another context. This is useful in NLP applications where preserving meaning is a desideratum.