On teaching AI and expert systems courses

García, Oscar N.; Pérez, Rafael; Silverman, Barry G.; Austin, Henry Stewart; Baum, R.; Brady, LI; Cameron, Rich; Castañeda, Serrano; Chen, J.; Dey, Pradip Peter; DiCristina, G.M.; Elmaghraby, Adel; Foster, Rachel A.; Freeman, Carol; Kirch, M.R.; Lawrence, Alan; Manesh, A.; Manickam, Shan; Ramamoorthy, C. V.; Rariden, Robert L.; Reichenbach, U.; Rosenbaum, Susan; Saner, F.E.; Torsone, C.M.; Valentine, D. W.; Landingham, H.F. Van; Vásquez, Restrepo

doi:10.1109/13.204845

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Inference Engine: The inference engine is the program that locates the appropriate knowledge in the knowledge base, and infers new knowledge by applying logical processing and problem-solving strategies [39].…”

Section: Every Expert System Consists Of Two Principal Partsmentioning

confidence: 99%

Advanced Applications of Neural Networks and Artificial Intelligence: A Review

Kumar¹,

Thakur²

2012

IJITCS

110

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Artificial Neural Network is a branch of Artificial intelligence and has been accepted as a new computing technology in computer science fields. This paper reviews the field of Artificial intelligence and focusing on recent applications which uses Artificial Neural Networks (ANN"s) and Artificial Intelligence (AI). It also considers the integration of neural networks with other computing methods Such as fuzzy logic to enhance the interpretation ability of data. Artificial Neural Networks is considers as major soft-computing technology and have been extensively studied and applied during the last two decades. The most general applications where neural networks are most widely used for problem solving are in pattern recognition, data analysis, control and clustering. Artificial Neural Networks have abundant features including high processing speeds and the ability to learn the solution to a problem from a set of examples. The main aim of this paper is to explore the recent applications of Neural Networks and Artificial Intelligence and provides an overview of the field, where the AI & ANN"s are used and discusses the critical role of AI & NN played in different areas.

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Section: Every Expert System Consists Of Two Principal Partsmentioning

confidence: 99%

Advanced Applications of Neural Networks and Artificial Intelligence: A Review

Kumar¹,

Thakur²

2012

IJITCS

110

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“…Logic programming languages are one of the most successful examples of declarative languages; they have a formal declarative semantics, as well as sound and complete corresponding operational semantics. They have a notable number of practical applications, are widely used in Artificial Intelligence, and are often used in university courses as a valid tool to support learning of AI techniques [12].…”

Section: Introductionmentioning

confidence: 99%

SLDNF-Draw: Visualization of Prolog operational semantics in LaTeX1

Gavanelli

2017

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Logic Programming is a programming paradigm widely used for teaching Artificial Intelligence in university courses. Prolog, probably the most popular Logic Programming language, is based on inferences similar to theorem provers, and learning it is propaedeutical for understanding formal specification languages.However, after a first phase in which students learn how specifications can be translated into executable code, a second phase is necessary for engineering the resulting program. In this second phase, having a clear picture of the execution model is crucial.In this paper, SLDNF-Draw, a program that visualizes the SLDNF operational semantics of Prolog, is presented. SLDNF-Draw produces trees in L A T E X, the popular typesetting system, so that the generated trees have very high typographic quality and can be embedded in printed documents (e.g., course handouts) as well as in animated presentations.SLDNF Draw is completely written in Prolog, as a meta-interpreter, and does not rely on external imperative languages for the visualization.

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ASPECT: Answer Set rePresentation as vEctor graphiCs in laTex

Bertagnon,

Gavanelli

2024

Journal of Logic and Computation

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Logic programming is a declarative programming paradigm that finds extensive use in the field of Artificial Intelligence (AI). As a result, it has become a valuable tool used in university courses for teaching students AI techniques. Besides Prolog language, the more recent Answer Set Programming (ASP) language turns out to be a powerful tool for developing advanced applications due to the expressiveness of the language and the availability of efficient solving systems. Unfortunately, the output of ASP solvers can be difficult to interpret, since it is a set of atoms, often long and verbose. This is most true in the case of students learning the language or in the case of experts developing applications for complex real-world problems. For these reasons, the ability to produce, when possible, a graphical representation of the solver output becomes useful to ensure easier interpretation of the results. In this paper we present ASPECT, a sub-language of ASP in which the user can directly define, in an intuitive and declarative way, a graphical representation of the answer set. The ASPECT atoms can be converted into the popular LaTeX markup language to produce vector graphics. The documents produced by ASPECT are easy to embed in documents such as scientific articles, course handouts and presentations. Also, the development of user-friendly interfaces is critical for wider use of similar technologies in the industrial sector as well. Moreover, ASPECT is also extended to deal with temporal information, and provide graphical animations of answer sets that enclose the temporal dimension, such as in planning problems. Finally, we advocate the use of ASPECT to create complex and animated presentations starting from a declarative specification.

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On teaching AI and expert systems courses

Cited by 4 publications

References 22 publications

Advanced Applications of Neural Networks and Artificial Intelligence: A Review

Advanced Applications of Neural Networks and Artificial Intelligence: A Review

SLDNF-Draw: Visualization of Prolog operational semantics in LaTeX1

ASPECT: Answer Set rePresentation as vEctor graphiCs in laTex

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