document: Towards an ontology of approximate reason This article introduces structural aspects in an ontology of approximate reason . The basic assumption in this ontology is that approximate reason is a capability of an agent . Agents are designed to classify information granules derived from sensors that respond to stimuli in the environment of an agent or received from other agents . Classification of information granules is carried out in the context of parameterized approximation spaces and a calculus of granules . Judgment in agents is a faculty of thinking about -LRB- classifying -RRB- the particular relative to decision rules derived from data . Judgment in agents is reflective , but not in the classical philosophical sense -LRB- e.g. , the notion of judgment in Kant -RRB- . In an agent , a reflective judgment itself is an assertion that a particular decision rule derived from data is applicable to an object -LRB- input -RRB- . That is , a reflective judgment by an agent is an assertion that a particular vector of attribute -LRB- sensor -RRB- values matches to some degree the conditions for a particular rule . In effect , this form of judgment is an assertion that a vector of sensor values reflects a known property of data expressed by a decision rule . Since the reasoning underlying a reflective judgment is inductive and surjective -LRB- not based on a priori conditions or universals -RRB- , this form of judgment is reflective , but not in the sense of Kant . Unlike Kant , a reflective judgment is surjective in the sense that it maps experimental attribute values onto the most closely matching descriptors -LRB- conditions -RRB- in a derived rule . Again , unlike Kant 's notion of judgment , a reflective judgment is not the result of searching for a universal that pertains to a particular set of values of descriptors . Rather , a reflective judgment by an agent is a form of recognition that a particular vector of sensor values pertains to a particular rule in some degree . This recognition takes the form of an assertion that a particular descriptor vector is associated with a particular decision rule . These considerations can be repeated for other forms of classifiers besides those defined by decision rules
doc_bio_tags: O O B O B I O O O O O O O B O B I O O O O O O B O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I I O O O O B O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I
extractive_keyphrases: ontology approximate reason information granules parameterized approximation spaces granules decision rules reflective judgment
abstractive_keyphrases: pattern recognition rough sets