A review of case-based reasoning in cognition–action continuum: a step toward bridging symbolic and non-symbolic artificial intelligence

Abstract

In theories and models of computational intelligence, cognition and action have historically been investigated on separate grounds. We conjecture that the main mechanism of case-based reasoning (CBR) applies to cognitive tasks at various levels and of various granularity, and hence can represent a bridge—or a continuum—between the higher and lower levels of cognition. CBR is an artificial intelligence (AI) method that draws upon the idea of solving a new problem reusing similar past experiences. In this paper, we re-formulate the notion of CBR to highlight the commonalities between higher-level cognitive tasks such as diagnosis, and lower-level control such as voluntary movements of an arm. In this view, CBR is envisaged as a generic process independent from the content and the detailed format of cases. Diagnostic cases and internal representations underlying motor control constitute two instantiations of the case representation. In order to claim such a generic mechanism, the account of CBR needs to be revised so that its position in non-symbolic AI becomes clearer. The paper reviews the CBR literature that targets lower levels of cognition to show how CBR may be considered as a step toward bridging the gap between symbolic and non-symbolic AI.

Defining the Initial Case-Base for a CBR Operator Support System in Digital Finishing

“…An initial validation at one of the companies shows that operators recognize and understand the CBR system inputs and outputs. This serves as an example use case that works toward solving the problems highlighted by [17]. With regards to the main motivating hypothesis of this work initial tests also shows increase in the operation of the machine that is augmented by the CBR system.…”

“…Öztürk and Tideman's [17] statement "We believe case grounding problem is the reason why CBR has not seen wide-spread adoption in the industrybecause manual extraction of cases from reports and records is costly and time consuming" is one of the main reasons why we report our approach related to knowledge acquisition. We do agree that it is a time consuming effort, though, when consulting existing literature for knowledge acquisition for CBR to learn how to extract and categorize the relevant information, we did not find any clear guidelines or methodological descriptions for case grounding.…”

“…Öztürk and Tideman say in their 2014 review paper [17]: "Initial population of a case base is a daunting task in classical CBR because it is manually crafted by knowledge engineers who make use of domain experts or written material to extract the case content. ... We believe case grounding problem is the reason why CBR has not seen wide-spread adoption in the industry -because manual extraction of cases from reports and records is costly and time consuming".…”

Artificial intelligence and robotics as the driving power of modern society

“…The term artificial intelligence had been closely related to the field of "symbolic AI," which was widespread until the late 1980s [5]. To overcome some of the limitations of symbolic artificial intelligence, subsymbolic (or non-symbolic) methodologies [6] such as neural networks, fuzzy systems, evolutionary computing, and other brain-like computational models began to gain popularity, leading to the emergence of the term "computer intelligence" as part of the AI.…”