Knowledge is a choice relation

According to this analysis, justified, true belief is necessary and sufficient for knowledge. The Tripartite Analysis of Knowledge: S knows that p iff p is true; S believes that p; S is justified in believing that p.

Knowledge is a choice relation

A KR is a Surrogate Any intelligent entity that wishes to reason about its world encounters an important, inescapable fact: A program or person engaged in planning the assembly of a bicycle, for instance, may have to reason about entities like wheels, chains, sprockets, handle bars, etc.

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This unavoidable dichotomy is a fundamental rationale and role for a representation: Operations on and with representations substitute for operations on the real thing, i. In this view reasoning itself is in part a surrogate for action in the world, when we can not or do not yet want to take that action.

Knowledge is a choice relation

The first question about any surrogate is its intended identity: There must be some form of correspondence specified between the surrogate and its intended referent in the world; the correspondence is the semantics for the representation. The second question is fidelity: What attributes of the original does it capture and make explicit, and which does it omit?

Perfect fidelity is in general impossible, both in practice and in principle. It is impossible in principle because any thing other than the thing itself is necessarily different from the thing itself in location if nothing else. Put the other way around, the only completely accurate representation of an object is the object itself.

All other representations are inaccurate; they inevitably contain simplifying assumptions and possibly artifacts. Two minor elaborations extend this view of representations as surrogates.

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First, it appears to serve equally well for intangible objects as it does for tangible objects like gear wheels: Representations function as surrogates for abstract notions like actions, processes, beliefs, causality, categories, etc.

Second, formal objects can of course exist inside the machine with perfect fidelity: Mathematical entities, for example, can be captured exactly, precisely because they are formal objects.

Since almost any reasoning task will encounter the need to deal with natural objects i. Imperfect surrogates mean incorrect inferences are inevitable Two important consequences follow from the inevitability of imperfect surrogates.

One consequence is that in describing the natural world, we must inevitably lie, by omission at least. At a minimum we must omit some of the effectively limitless complexity of the natural world; our descriptions may in addition introduce artifacts not present in the world.

The second and more important consequence is that all sufficiently broad-based reasoning about the natural world must eventually reach conclusions that are incorrect, independent of the reasoning process used and independent of the representation employed.

Sound reasoning cannot save us: If the world model is somehow wrong and it must be some conclusions will be incorrect, no matter how carefully drawn. A better representation cannot save us: The significance of the error may of course vary; indeed much of the art of selecting a good representation is in finding one that minimizes or perhaps even eliminates error for the specific task at hand.

But the unavoidable imperfection of surrogates means we can supply at least one guarantee for any entity reasoning in any fashion about the natural world: Drawing only sound inferences thus does not free reasoning from error; it can only ensure that inference is not the source of that error.

Given that broad based reasoning will inevitably be wrong, the step from sound inference to other models of inference is thus not a move from total accuracy to error, but is instead a question of balancing off the possibility of one more source of error against the gains e. We do not suggest that unsound reasoning ought to be embraced casually, but do claim that, given the inevitability of error even with sound reasoning, it makes sense to evaluate pragmatically the relative costs and benefits that come from using both sound and unsound reasoning methods.

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A KR is a Set of Ontological Commitments If, as we have argued, all representations are imperfect approximations to reality, each approximation attending to some things and ignoring others, then in selecting any representation we are in the very same act unavoidably making a set of decisions about how and what to see in the world.

That is, selecting a representation means making a set of ontological commitments. It is unavoidably so because of the inevitable imperfections of representations. It is usefully so because judicious selection of commitments provides the opportunity to focus attention on aspects of the world we believe to be relevant.

Knowledge is a choice relation

The focusing effect is an essential part of what a representation offers, because the complexity of the natural world is overwhelming. We and our reasoning machines need guidance in deciding what in the world to attend to and what to ignore. The glasses supplied by a representation can provide that guidance: In telling us what and how to see, they allow us to cope with what would otherwise be untenable complexity and detail.

Hence the ontological commitment made by a representation can be one of the most important contributions it offers. There is a long history of work attempting to build good ontologies for a variety of task domains, including early work on an ontology for liquids [12]the lumped element model widely used in representing electronic circuits e.The relation between propositional knowledge and the knowledge at issue in other “knowledge” locutions in English, such as knowledge-where (“Susan knows where she is”) and especially knowledge-how (“Susan knows how to ride a bicycle”) is subject to some debate (see Stanley and his opponents discussed therein).

Knowledge, skills, and abilities (aka KSAs) are three different things. And it’s important to know the difference – even though the difference can be subtle.

Knowledge is the theoretical or practical understanding of a subject. Distant Supervision for Relation Extraction with an Incomplete Knowledge Base Bonan Min, Ralph Grishman, Li Wan New York University New York, NY {min,grishman,wanli} as a popular choice for training relation ex-tractors.

Knowledge representation and reasoning - Wikipedia

In this paper, we show that a sig-. Aug 20,  · In this complex, global business environment, these types of knowledge management programs can help managers embrace change and encourage ideas and insight, which often lead to innovation, even.

Knowledge, being a primitive fact of consciousness, cannot, strictly speaking, be defined; but the direct and spontaneous consciousness of knowing may be made clearer by pointing out its essential and distinctive characteristics.

Knowledge representation and reasoning (KR, KR², KR&R) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language.