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The engineering of creativity: a review of Boden's the creative mind

Roger C. Schank, David A. Foster
1995 Artificial Intelligence  
Why creativity is important to explain Creativity is important to understand and explain because: ( 1) It is a natural phenomenon we observe in human beings, and thus deserves a rigorous explanation.  ...  There are, however, potential pitfalls that appear when one uses the word "science", and it is important to carefully avoid them.  ... 
doi:10.1016/0004-3702(95)90028-4 fatcat:ecymsg4zl5hs7b7nbfywbppj4a

Anthropomorphism and the social robot

Brian R. Duffy
2003 Robotics and Autonomous Systems  
This propensity to anthropomorphise is not seen as a hindrance to social robot development, but rather a useful mechanism that requires judicious examination and employment in social robot research.  ...  This paper discusses the issues pertinent to the development of a meaningful social interaction between robots and people through employing degrees of anthropomorphism in a robot's physical design and  ...  [2] , but in order to "solve" AI through hard research, it is a simpler more justifiable route to take.  ... 
doi:10.1016/s0921-8890(02)00374-3 fatcat:sbonacc3hzabrj6dfixhzl2dna

After the Philosophy of Mind: Replacing Scholasticism with Science*

Anthony Chemero, Michael Silberstein
2008 Philosophy of Science  
The second is over explanatory style: should explanation in cognitive and neural science be reductionist-mechanistic, interlevel mechanistic, or dynamical?  ...  We provide a taxonomy of the two most important debates in the philosophy of the cognitive and neural sciences.  ...  It is worth pausing to appreciate why nonlinearity is potentially bad news for mechanistic explanation.  ... 
doi:10.1086/587820 fatcat:a57xqodpk5d4vdbbys4vlruhwe

Some issues concerning optîmality and diachronic adaptation

Martin Haspelmath
1999 Zeitschrift für Sprachwissenschaft  
The least interesting rhetorical point that both Haid» and Müller make is that functional explanations appeal to common sense.  ...  Müller rightly notes that functional explanations run the risk of being post hoc and arbitrary, but I cannot see why this should be a greater problem for functionalist analyses than for, say, generative  ... 
doi:10.1515/zfsw.1999.18.2.251 fatcat:6kxhntelojdnvo5oox2dqe5l5e

The Who in Explainable AI: How AI Background Shapes Perceptions of AI Explanations [article]

Upol Ehsan, Samir Passi, Q. Vera Liao, Larry Chan, I-Hsiang Lee, Michael Muller, Mark O. Riedl
2021 arXiv   pre-print
Explainability of AI systems is critical for users to take informed actions and hold systems accountable.  ...  By bringing conscious awareness to how and why AI backgrounds shape perceptions of potential creators and consumers in XAI, our work takes a formative step in advancing a pluralistic Human-centered Explainable  ...  Although there is a current lack of consensus on the meaning of explainability and related terms such as interpretability [7, 121] , XAI work shares a common goal of making the AI systems' decisions or  ... 
arXiv:2107.13509v1 fatcat:si6dwi57njg27fkk6hqyqpunce

Annotated bibliography on research methodologies

Yoram Reich
1994 Artificial intelligence for engineering design, analysis and manufacturing  
It is almost impossible to clearly and accurately state what AI programs do and why. It is equally hard, therefore, to replicate such programs.  ...  (Lenat and Brown, 1984) This paper responses to the critique in (Ritchie and Hanna, 1984) . It explains why AM appeared to work and why its adhocness may be its source of power.  ... 
doi:10.1017/s0890060400001013 fatcat:lveoeq4gajgnjaz7fkg2tawlma

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

Guang Yang, Qinghao Ye, Jun Xia
2021 Information Fusion  
Many of the machine learning algorithms cannot manifest how and why a decision has been cast. This is particularly true of the most popular deep neural network approaches currently in use.  ...  Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made.  ...  to train a student model, which is usually explainable, with a teacher model, which is hard to interpret.  ... 
doi:10.1016/j.inffus.2021.07.016 pmid:34980946 pmcid:PMC8459787 fatcat:3rmzvn72dbgglcddgolce2xsfe

Metacognition in computation: A selected research review

Michael T. Cox
2005 Artificial Intelligence  
I examine metacognition with respect to both problem solving (e.g., planning) and to comprehension (e.g., story understanding) processes of cognition.  2005 Published by Elsevier B.V.  ...  Various disciplines have examined the many phenomena of metacognition and have produced numerous results, both positive and negative.  ...  ., [136, 185] ), AI and cognitive psychology present the most thorough mechanistic explanations for such phenomena.  ... 
doi:10.1016/j.artint.2005.10.009 fatcat:6oghpyu5wrbe3djr6he4wvmx2y

Theoretical status of computational cognitive modeling

Ron Sun
2009 Cognitive Systems Research  
It also connects this issue with a number of other relevant issues, such as the general relationship between theory and data, the validation of models, and the practical benefits of computational modeling  ...  In this discussion, this article examines various (existent or possible) positions on this issue and argues in favor of the view above.  ...  Acknowledgments This work was carried out while the author was supported in part by Army Research Institute Contracts DASW01-00-K-0012 and W74V8H-05-K-0002 (to Ron Sun and Bob Mathews) and ONR Grant N00014  ... 
doi:10.1016/j.cogsys.2008.07.002 fatcat:4mqoyxjvlvdhbjfiuhvs6ff2ae

The 30-Year Cycle In The AI Debate [article]

Jean-Marie Chauvet
2018 arXiv   pre-print
The rapid changes in these everyday work and entertainment tools have fueled a rising interest in the underlying technology itself; journalists write about AI tirelessly, and companies -- of tech nature  ...  or not -- brand themselves with AI, Machine Learning or Deep Learning whenever they get a chance.  ...  Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman?  ... 
arXiv:1810.04053v1 fatcat:ghdlrrvjojaxxbwmn5daz3fkee

Introduction to the JAGI Special Issue "On Defining Artificial Intelligence" —Commentaries and Author's Response

Dagmar Monett, Colin W. P. Lewis, Kristinn R. Thórisson
2020 Journal of Artificial General Intelligence  
The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressly or implied, of AFOSR or the U.S.  ...  Acknowledgments We want to thank all authors for their time, their commitment, and the high quality of their contributions.  ...  It is more when we are concerned with "building a general AI" (or AGI) that having a common and well defined goal becomes important for many of the reasons that this paper explains.  ... 
doi:10.2478/jagi-2020-0003 fatcat:4q5toqhjnjcr3nem2ek4rmp5li

Adaptive rationality and identifiability of psychological processes

Dominic W. Massaro, Daniel Friedman
1991 Behavioral and Brain Sciences  
of achieving goals and the cost of both external and mental problem-solving search.  ...  cost of hypothesis formation; in (3) casual inference the trade-off is between accuracy in predicting future events and the cost of hypothesis formation; and in (4) problem solving it is between the probability  ...  Finding the best explanation for a class of independent problems using probability theory (and several other forms of abduction) is NP-hard (Bylander et al. 1989) .  ... 
doi:10.1017/s0140525x00070977 fatcat:uqq3i4gfnbgrldqruurvb6xila

Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail

Marcin Miłkowski, Witold M. Hensel, Mateusz Hohol
2018 Journal of Computational Neuroscience  
In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model  ...  In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution  ...  We are also grateful to Carlos Zednik and two Anonymous Reviewers for their valuable remarks.  ... 
doi:10.1007/s10827-018-0702-z pmid:30377880 pmcid:PMC6306493 fatcat:6me6trtcjnhedo3pl5ms5jmoom

A 20-Year Community Roadmap for Artificial Intelligence Research in the US [article]

Yolanda Gil, Bart Selman
2019 arXiv   pre-print
The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy  ...  Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment.  ...  must be able to explain why his bedside manner is or is not ideal.  ... 
arXiv:1908.02624v1 fatcat:jza6i2tzufgeracsou77qukbu4

Machine behaviour

Iyad Rahwan, Manuel Cebrian, Nick Obradovich, Josh Bongard, Jean-François Bonnefon, Cynthia Breazeal, Jacob W. Crandall, Nicholas A. Christakis, Iain D. Couzin, Matthew O. Jackson, Nicholas R. Jennings, Ece Kamar (+11 others)
2019 Nature  
Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms.  ...  We first outline a set of questions that are fundamental to this emerging field and then explore the technical, legal and institutional constraints on the study of machine behaviour.  ...  Mechanism (causation) Mechanistic explanations for what the behaviour is, and how it is constructed, including computational mechanisms or external stimuli that trigger it.  ... 
doi:10.1038/s41586-019-1138-y pmid:31019318 fatcat:g6g6ztreqbb6rlep5cpstu3sbi
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