The Basics of Knowledge Representation and Reasoning

Abstract

A widely recognized goal of artificial intelligence (AI) is the creation of artifacts that can emulate humans in their ability to reason symbolically, as exemplified in typical AI domains such as planning, natural language understanding, diagnosis, and tutoring. Currently most of this work is predicated on a belief that intelligent systems can be constructed from explicit, declarative knowledge bases, which in turn are operated on by general, formal reasoning mechanisms. This fundamental hypothesis of AI means that knowledge representation and reasoning—the study of formal ways of extracting information from symbolically represented knowledge—is of central importance to the field. In this article, we review some of the basics of this important research area and briefly survey the kinds of techniques typically used for representation in AI programs. We also consider some important current research directions.

Long-term assessment of a service robot in a hotel environment

“…Architecture design has 105 attracted the specific attention of the scientific community over the last few years, where different architecture paradigms have been developed (e.g. reactive, deliberative and hybrid [12] [5], interfaces, human-machine communication systems including dialogue systems [21], recognition systems [25], cognitive modeling [35] and knowledge representation [3]. …”

Representing Knowledge in Robotic Systems with KnowLang

“…The inference algorithms will be based on reasoning algorithms relying on First Order Logic (FOL) [5] (and its extensions), First Order Probabilistic Logic (FOPL) [15] and on Description Logics (DL) [6]. FOPL increases the power of FOL by allowing us to assert in a natural way "likely" features of objects and concepts via a probability distribution over the possibilities that we envision.…”

“…A knowledge model may classify knowledge elements by type, and group those of the same type into collections. Typical knowledge representation techniques are rules, frames, semantic networks, concept diagrams, ontologies and logics [4,5]. Actually logics are used to formalise the knowledge representation techniques, which gives them a precise semantics.…”

“…For example, if the KB contains rules, the inference engine may chain them either forward (e.g., for forecast) or backward (e.g., for diagnosis). The inference engines are logic-based, e.g., First Order Logic (FOL) and Description Logics (DL) [5,6].…”

RACCOON: A Connection Reasoner for the Description Logic ALC

“…In the preprocessing phase, clauses with pure literals, i.e., the ones without complements in the matrix, are eliminated (Brachman and Levesque, 2004). The only optimization integrated is regularity (Letz et al, 1994), which is already successfully used in the CM for FOL.…”