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Artificial Intelligence Software and Hardware Platforms
Published in Mazin Gilbert, Artificial Intelligence for Autonomous Networks, 2018
Rajesh Gadiyar, Tong Zhang, Ananth Sankaranarayanan
In contrast to machine learning systems, a reasoning system is a software system that generates conclusions from available knowledge using logical techniques, such as deduction and induction [1]. Reasoning systems play an important role in the implementation of AI and knowledge-based systems. There are two types of reasoning systems: (1) memory-based reasoning and (2) logic-based reasoning. Memory-based reasoning is to identify similar cases from past experience or memory, and to apply the information from the past experience to the problem at hand. Logic-based reasoning aims at learning rule-based knowledge, called hypotheses, from observations (positive and negative), using existing background knowledge and integrity constraints.
Exploring Reasoning for Utilizing the Full Potential of Semantic Web
Published in Archana Patel, Narayan C. Debnath, Bharat Bhushan, Semantic Web Technologies, 2023
Ayesha Ameen, Khaleel Ur Rahman Khan, B. Padmaja Rani
Reasoning system based on rules comprises rules, rule language, and rule engine. The rule is the basis of inference execution in a knowledge-based system; in Semantic Web, a rule represents a logical entailment among a set of formulas called premises and an assertion called a conclusion. Rule general formula is as follows: A1,A2,An→B
Smart Knowledge Engineering for Cognitive Systems: A Brief Overview
Published in Cybernetics and Systems, 2022
Caterine Silva de Oliveira, Cesar Sanin, Edward Szczerbicki
The ultimate goal of the SKECS approach is to allow cognitive systems to augment their intelligence. Augmented intelligence follows a five-function cadence that allows it to learn with human influence. It repeats a cycle of understanding, interpretation, reasoning, learning, and assurance. As yet, the knowledge augmentation layer has been fully explored, but steps have been taken on the road toward that objective. By the continuous learning process proposed in the fourth layer, where the human is placed in the center, experiential knowledge can be incrementally incorporated (de Oliveira, Sanin, and Szczerbicki 2020b). This will have an impact on the entire reasoning system, increase its specificity, and generate more certainty during decision-making. Therefore, the concepts and technologies suggested previously can be used as a pathway in the direction of augmented intelligence in cognitive applications (directing the whole AI–human system toward wiser decision-making).