An Introduction to Expert Systems

By A. DeTore, V. President. 1989

Subfields: Knowledge Graphs & Representation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Knowledge Graphs & Representation

1. Gesina Schwalbe
2. David Madigan
3. Alun David Preece
4. Benjamin Letham
5. Bettina Finzel
6. Cynthia Rudin

Citations in the corpus

• Galactica: A Large Language Model for Science (2022, background): “If true, we could make more purposefully-designed LLMs by having a clear understanding of what enters the corpus, similar to expert systems which had normative standards (Jackson, 1990).”
• A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts (2021, background): “Inherently transparent approaches paved the way for expert systems that were designed to support human decision-makers (see [70]).”
• Asking 'Why' in AI: Explainability of intelligent systems - perspectives and challenges (2018, methodology): “The ‘classical’ period of progress in AI — from the 1970s to early 1990s — featured a corresponding phase of interest in methods for explanation generation in largely symbolic reasoning systems, including so-called ‘expert systems’ (Jackson (1999)). Significant progress was made on explainability du”
• Learning theory analysis for association rules and sequential event prediction (2013, background): “The rules provide “IF,THEN,ELSE” conditions, and yield models of the same form as those from the expert systems literature from the early days of artificial intelligence (Jackson, 1998).”
• A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, unknown): “The important features which distinct ES from other mathematical models could be summarized as follows [127]: (a) ES are not limited by rigid mathematical or analog schemes and can handle factual or heuristic knowledge; (b) The knowledge base can be continuously augmented as necessary with accumulat”
• An Introduction to MultiAgent Systems (2002, unknown): “An expert system is one that is capable of solving problems or giving advice in some knowledge-rich domain (Jackson, 1986).”
• Stakeholders in Explainable AI (2018, unknown): “It was soon realised that explanations were necessary for two distinct reasons: system development, particularly testing, and engendering end-user trust (Jackson 1999).”
• Interactive and interpretable machine learning models for human machine collaboration (2015, unknown): “An expert system is defined as a computer system that emulates the decision-making abilities of human experts [75] and is of particular to the fields of medicine [120], fault diagnosis [3] and science [135].”