Reasoning in Non-Axiomatic Logic: A Case Study in Medical Diagnosis

Wang, Pei; Awan, Seemal F

doi:10.1007/978-3-642-22887-2_33

Cited by 7 publications

(21 citation statements)

References 9 publications

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“…I agree with Fox (2020) that "Medicine draws on a vast diversity of knowledge and human skills and requires many different forms of intelligence." We did some experiments in that domain (Wang and Awan, 2011), and have been working on it in recent years.…”

Section: Fruitfulnessmentioning

confidence: 99%

Special Issue “On Defining Artificial Intelligence”—Commentaries and Author’s Response

Monett

Lewis²,

Þórisson

et al. 2020

Journal of Artificial General Intelligence

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Pei Wang's paper titled "On Defining Artificial Intelligence" was published in a special issue of the Journal of Artificial General Intelligence (JAGI) in December of last year (Wang, 2019). Wang has been at the forefront of AGI research for over two decades. His non-axiomatic approach to reasoning has stood as a singular example of what may lie beyond narrow AI, garnering interest from NASA and Cisco, among others. We consider his article one of the strongest attempts, since the beginning of the field, to address the long-standing lack of consensus for how to define the field and topic of artificial intelligence (AI). In the recent AGISI survey on defining intelligence (Monett and Lewis, 2018), Pei Wang's definition, The essence of intelligence is the principle of adapting to the environment while working with insufficient knowledge and resources. Accordingly, an intelligent system should rely on finite processing capacity, work in real time, open to unexpected tasks, and learn from experience. This working definition interprets "intelligence" as a form of "relative rationality" (Wang, 2008), 1. Most striking in these numbers is the glaring absence of female authors. A common reason among female academics for rejecting our invitation to contribute was overcommitment. As a community, we may want to think of new, different ways of engaging the full spectrum of AI practitioners if we value inclusion as an essential constituent of a healthy scientific growth. Self determination and willingness to participate are also essential. This is an open access article licensed under the Creative Commons BY-NC-ND License.

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Section: Fruitfulnessmentioning

confidence: 99%

Special Issue “On Defining Artificial Intelligence”—Commentaries and Author’s Response

Monett

Lewis²,

Þórisson

et al. 2020

Journal of Artificial General Intelligence

Self Cite

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“…NARS (non-axiomatic reasoning system) is an AGI-designed framework of a reasoning system. The project has been described in many publications, including two books (Wang, 2006 , 2013 ), so it is only briefly summarized here.…”

Section: Nars Overviewmentioning

confidence: 99%

“…As a reasoning system, NARS uses a formal language called “Narsese” for knowledge representation, which is defined by a formal grammar given in the study by Wang ( 2013 ). To fully specify and explain this language is beyond the scope of this article, so in the following, only the directly relevant part is introduced informally and described briefly.…”

Section: Nars Overviewmentioning

confidence: 99%

“…Hence, in NARS, the truth of each statement and the meaning of each term are grounded on nothing but the system’s experience. The formal definition of this semantics and discussions of its implications can be found in the studies by Wang ( 2005 , 2013 ) and are only briefly summarized in the following.…”

Section: Nars Overviewmentioning

confidence: 99%

“…The value c is called the confidence of the statement and is defined as the proportion of current evidence among total amount of evidence at a moment in the future after new evidence of a certain amount is collected, that is, c = w /( w + k ), where k ≥ 1. This constant k is a “personality parameter” and is explained further in the study by Wang ( 2013 ). The value of k can be seen as a unit of evidence that decides how fast the c value increases as new evidence comes, and in the following, we use the default k = 1 to simplify the discussion.…”

Section: Nars Overviewmentioning

confidence: 99%

See 2 more Smart Citations

Self in NARS, an AGI System

Wang

Hammer

2018

Front. Robot. AI

Self Cite

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This article describes and discusses the self-related mechanisms of a general-purpose intelligent system, NARS. This system is designed to be adaptive and to work with insufficient knowledge and resources. The system's various cognitive functions are uniformly carried out by a central reasoning-learning process following a "non-axiomatic" logic. This logic captures the regularities of human empirical reasoning, where all beliefs are revisable according to evidence, and the meaning of concepts are grounded in the system's experience. NARS perceives its internal environment basically in the same way as how it perceives its external environment although the sensors involved are completely different. Consequently, its self-knowledge is mostly acquired and constructive, while being incomplete and subjective. Similarly, self-control in NARS is realized using mental operations, which supplement and adjust the automatic inference control routine. It is argued that a general-purpose intelligent system needs the notion of a "self," and the related knowledge and functions are developed gradually according to the system's experience. Such a mechanism has been implemented in NARS in a preliminary form.

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Primum Non Nocere: The Ethical Beginnings of a Non-Axiomatic Reasoning System

Ireland

2023

Lecture Notes in Computer Science

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Reasoning in Non-Axiomatic Logic: A Case Study in Medical Diagnosis

Cited by 7 publications

References 9 publications

Special Issue “On Defining Artificial Intelligence”—Commentaries and Author’s Response

Special Issue “On Defining Artificial Intelligence”—Commentaries and Author’s Response

Self in NARS, an AGI System

Primum Non Nocere: The Ethical Beginnings of a Non-Axiomatic Reasoning System

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