Logic-Based Reasoning in the Framework of Artificial Intelligence

Author(s):  
Xenia Naidenova

This chapter focuses on the tasks of knowledge engineering related mainly to knowledge acquisition and modeling integrated logic-based inference. We have overlooked the principal and more important directions of researches that pave the ways to understanding and modeling human plausible (commonsense) reasoning in computers.

Author(s):  
JACQUES H.J. LENTING ◽  
PETER J. BRASPENNING

Since its introduction in 1969, the phrase “frame problem” has been attributed various interpretations. Most researchers in the field of Artificial Intelligence define the frame problem as the problem of finding an effective representation for reasoning about change. Logicians use the phrase to refer to a much less general technical problem within logic whereas philosophers tend to interpret the phrase as the more general problem of determining (ir)relevance. All in all, this discrepancy has led to considerable confusion about the meaning of the phrase. We contend that most of this confusion can be avoided, if the original (robotics) context of the frame problem is adhered to. We present an engineering view on the frame problem that allows us to strip the frame problem from associated problem notions like qualification and ramification. The problem that remains is intimately related to the knowledge acquisition bottleneck in knowledge engineering.


Author(s):  
JOSÉ ELOY FLÓREZ ◽  
JAVIER CARBÓ ◽  
FERNANDO FERNÁNDEZ

Knowledge-based systems (KBSs) or expert systems (ESs) are able to solve problems generally through the application of knowledge representing a domain and a set of inference rules. In knowledge engineering (KE), the use of KBSs in the real world, three principal disadvantages have been encountered. First, the knowledge acquisition process has a very high cost in terms of money and time. Second, processing information provided by experts is often difficult and tedious. Third, the establishment of mark times associated with each project phase is difficult due to the complexity described in the previous two points. In response to these obstacles, many methodologies have been developed, most of which include a tool to support the application of the given methodology. Nevertheless, there are advantages and disadvantages inherent in KE methodologies, as well. For instance, particular phases or components of certain methodologies seem to be better equipped than others to respond to a given problem. However, since KE tools currently available support just one methodology the joint use of these phases or components from different methodologies for the solution of a particular problem is hindered. This paper presents KEManager, a generic meta-tool that facilitates the definition and combined application of phases or components from different methodologies. Although other methodologies could be defined and combined in the KEManager, this paper focuses on the combination of two well-known KE methodologies, CommonKADS and IDEAL, together with the most commonly-applied knowledge acquisition methods. The result is an example of the ad hoc creation of a new methodology from pre-existing methodologies, allowing for the adaptation of the KE process to an organization or domain-specific characteristics. The tool was evaluated by students at Carlos III University of Madrid (Spain).


Author(s):  
Matteo Cristani

What is an ontology? Why is this relevant to a learning environment? It is quite well-established in recent investigations on information systems that formal ontologies area crucial problem to deal with, and in fact, received a lot of attention in several different communities, such as knowledge management, knowledge engineering, natural language processing, intelligent information integration, and so on (Fensel, 2000).Ontologies have been developed in artificial intelligence to facilitate knowledge sharing and reuse. The viewpoint we adopt here is taken from the general considerations on the use of philosophical issues in artificial intelligence: “the systematic, formal, axiomatic development of the logic of all forms and modes of being” (Cocchiarella,1991). Another commonly accepted definition is that an ontology is an explicit specification of a shared conceptualization that holds in a particular context.


Author(s):  
Matteo Cristani

What is an ontology? Why is this relevant to a learning environment? It is quite well-established in recent investigations on information systems that formal ontologies area crucial problem to deal with, and in fact, received a lot of attention in several different communities, such as knowledge management, knowledge engineering, natural language processing, intelligent information integration, and so on (Fensel, 2000).Ontologies have been developed in artificial intelligence to facilitate knowledge sharing and reuse. The viewpoint we adopt here is taken from the general considerations on the use of philosophical issues in artificial intelligence: “the systematic, formal, axiomatic development of the logic of all forms and modes of being” (Cocchiarella,1991). Another commonly accepted definition is that an ontology is an explicit specification of a shared conceptualization that holds in a particular context.


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