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The Artificial Intelligence Quotient : = Measuring Machine Intelligence Based on Multi-domain Complexity, Similarity, and Diversity.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	The Artificial Intelligence Quotient :/
其他題名:	Measuring Machine Intelligence Based on Multi-domain Complexity, Similarity, and Diversity.
作者:	Pereyda, Christopher Isaac.
面頁冊數:	1 online resource (231 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-09, Section: B.
Contained By:	Dissertations Abstracts International85-09B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9798381749793

The Artificial Intelligence Quotient : = Measuring Machine Intelligence Based on Multi-domain Complexity, Similarity, and Diversity.
Pereyda, Christopher Isaac.

The Artificial Intelligence Quotient :Measuring Machine Intelligence Based on Multi-domain Complexity, Similarity, and Diversity. - 1 online resource (231 pages)

Source: Dissertations Abstracts International, Volume: 85-09, Section: B.

Thesis (Ph.D.)--Washington State University, 2023.

Includes bibliographical references

The growth and development of AI systems and benchmarks have been rapidly increasing, yet there is a disproportionately small amount of examination into the domains used to evaluate these systems. Even the domains that consider a larger scope of evaluation are often not generalizable, and the implemented AI systems are often not usable for different domains. To address these issues, we propose a measure of machine intelligence that can be intuitively understood and effectively utilized. This measure utilizes new metrics for both the agents evaluated and the domains used in the evaluation. The intelligence of a given system is determined by measuring its performance on a multi-domain test with measurable complexity, similarity, and diversity. The Artificial Intelligence Quotient (AIQ) structures these domain side measurements into a clear and consistent framework that can be utilized to measure an AI system's intelligence. These domain side measurements allow for the creation of an intelligence space. Once a test is located within the space, its accompanying performance metric can be used to effectively scale the location and measuring an agent's capacitance, that is, the agent's capability to achieve a certain understanding of the domain. An agent that achieves a higher understanding of the domain will be given a higher AIQ score than one that achieves a lower understanding. Agent capacitance also scales with domain complexity. An agent that achieves a comparable understanding of a more complex domain will be given a higher AIQ score than an agent on a less complex domain. These measures and resulting AIQ scores are evaluated using several intuitive experiments. Once the measures are shown to be capturing what was intended, we construct test suites using the AIQ framework. These test suites are then be evaluated using known AI systems to validate the AIQ score is capturing an agent's intelligence.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381749793Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Artificial Intelligence QuotientIndex Terms--Genre/Form:

554714
Electronic books.

The Artificial Intelligence Quotient : = Measuring Machine Intelligence Based on Multi-domain Complexity, Similarity, and Diversity.
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