Review on Cognitive Architectures

Ganesha, D.; Venkatamuni, Vijayakumar Maragal

doi:10.17485/ijst/2017/v10i1/109392

Cited by 1 publication

(1 citation statement)

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“…Some of the most popular and cited cognitive architectures include ACT-R, Soar, CLARION, ICARUS, EPIC, and LIDA. Others in Duch et al (2008), Samsonovich (2010), Thórisson and Helgasson (2012), Goertzel et al (2014), and Ganesha and Venkatamuni (2017) provide additional surveys of the literature on cognitive architectures-a wide, interdisciplinary, and varied body of work. What becomes clear is the Pre-programmed behavioural responses Lowest 16 For example, Kahneman's ( 2011) system 1, system 2 dichotomy discussed in Thinking, Fast and Slow.…”

Section: Cognitive Architecturesmentioning

confidence: 99%

Cognitive architectures for artificial intelligence ethics

Bickley

Torgler

2022

AI & Soc

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As artificial intelligence (AI) thrives and propagates through modern life, a key question to ask is how to include humans in future AI? Despite human involvement at every stage of the production process from conception and design through to implementation, modern AI is still often criticized for its “black box” characteristics. Sometimes, we do not know what really goes on inside or how and why certain conclusions are met. Future AI will face many dilemmas and ethical issues unforeseen by their creators beyond those commonly discussed (e.g., trolley problems and variants of it) and to which solutions cannot be hard-coded and are often still up for debate. Given the sensitivity of such social and ethical dilemmas and the implications of these for human society at large, when and if our AI make the “wrong” choice we need to understand how they got there in order to make corrections and prevent recurrences. This is particularly true in situations where human livelihoods are at stake (e.g., health, well-being, finance, law) or when major individual or household decisions are taken. Doing so requires opening up the “black box” of AI; especially as they act, interact, and adapt in a human world and how they interact with other AI in this world. In this article, we argue for the application of cognitive architectures for ethical AI. In particular, for their potential contributions to AI transparency, explainability, and accountability. We need to understand how our AI get to the solutions they do, and we should seek to do this on a deeper level in terms of the machine-equivalents of motivations, attitudes, values, and so on. The path to future AI is long and winding but it could arrive faster than we think. In order to harness the positive potential outcomes of AI for humans and society (and avoid the negatives), we need to understand AI more fully in the first place and we expect this will simultaneously contribute towards greater understanding of their human counterparts also.

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