Challenges and Opportunities in Knowledge Representation and Reasoning

Challenges and Opportunities in Knowledge Representation and Reasoning

Pankaj Dadure, Partha Pakray, Sivaji Bandyopadhyay
Copyright: © 2023 |Pages: 14
DOI: 10.4018/978-1-7998-9220-5.ch148
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Abstract

Knowledge representation is of immense importance in the field of artificial intelligence and natural language processing. The representation of knowledge goes hand in hand with automated reasoning as one of the key goals of representing knowledge effectively is being able to reason about it. Researchers of knowledge representation and reasoning have built techniques and methods that are the main source of development in computer science and have made tremendous progress in a wide variety of real-life applications, ranging from natural language processing to robotics and software engineering. Further research is required in order to allow a more active role in guiding the reasoning process through the knowledge representation framework. This article has discussed knowledge representation and reasoning and analyzed the major challenges and new opportunities where novel knowledge representation and reasoning research have had a major impact.
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Background

The researchers of the AI community have believed that the knowledge in the human brain, and knowledge in intelligent information processing systems, is considered as a network of interconnected nodes. Moreover, the way nodes are organized, relations between the nodes and the effectiveness with which information is collected vary enormously in human brain networks and human knowledge systems. Network connections in the human brain provide different characteristics that lead to their rapid or slow recovery of information (Sandberg, 2013). So, there is a demand to design an intelligent knowledge representation system which ensure that an autonomous node can determine the appropriate connectivity. Furthermore, communication among nodes is not only an appointed string relationship, but also the network intelligence.

Figure 1.

Evolution of Knowledge Representation Techniques

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Relation Between Knowledge & Intelligence

In the modern era, knowledge plays a crucial role and leads to state-of-the-art decision-making techniques of artificial intelligence (Ackerman, 2005). It depicts the smart actions of AI agents or systems. Only with awareness or experience of the input is it possible for an individual or device to act correctly. The key issue of artificial intelligence lies in knowledge representation and reasoning: to recognize the essence of intelligence and cognition so well that computers can be programmed to show human skills.

Let’s take an example to understand this relationship:

Figure 2.

Decision-maker

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The example shown in Figure 2, there is one decision-maker whose actions are validated by understanding the environment using knowledge. But, if the knowledge part is discarded from here, it will not able to perform any intelligent action.

Key Terms in this Chapter

Irrationality: Irrationality is cognition, thinking, talking, or acting without inclusion of rationality.

Heterogeneity: A sample or population where each members have different characteristics.

Knowledge: The fact or condition of knowing something with familiarity gained through experience or association.

Inadequacy: A condition of being not enough or not good enough.

Knowledge Representation and Reasoning: It’s a process to encode human knowledge into a symbolic language so that it can be used by the information systems.

Uncertainty: Uncertainty refers to epistemic situations involving imperfect or unknown information.

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