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Mind, Introspection, and "The Objective"

Roger E. Bissell
2008 Journal of Ayn Rand studies  
and mental data (including mind) are objective and that causal efficacy of mind and mind-body interaction only make sense if mind is conceived of not as an attribute, but as an entity (viz., the conscious  ...  In this sequel to his essay "Ayn Rand and The Objective'" (JARS, Fall 2007), the author warns against "the seduction of 'the basic"' and uses ideas by Efron, Peikoff, and Aristotle to argue that introspection  ...  When people speak of mind-body interaction, surely this is what they mean, the efficient causal interaction of two entities.  ... 
doi:10.5325/jaynrandstud.10.1.0003 fatcat:fdll6zzhyvfgrkw3mp3di5thxa

The Future in Mind: Aspirations and Forward-Looking Behaviour in Rural Ethiopia

Bernard Tanguy, Stefan Dercon, Kate Orkin, Alemayehu Seyoum Taffesse
2014 Social Science Research Network  
Other, possibly more important, changes may have occurred in the mind-set of participants and led to changes in their behaviour.  ...  These results provide strong support for our findings regarding aspirations, and the changes in the mindset of people about their ability to affect their own lives.  ... 
doi:10.2139/ssrn.2514590 fatcat:5qdy3o3gg5g7ljzduebyr5run4

The effects of projected films on singers' expressivity in choral performance

Daniel J. Keown
2015 Psychomusicology: Music, Mind & Brain  
Based on these ratings, two choral etudes were identified that elicited the broadest congruency contrasts when paired with the film segments.  ...  After each performance, singers reported their level of self-expression. At the completion of all three performances, singers reported their preferred performance condition.  ...  off of the ground when naturally walking.  ... 
doi:10.1037/pmu0000095 fatcat:56cbccnjlzgd7fzrn4lyy6rghi

21st century skills, individual competences, personal capabilities and mind-sets related to sustainability: a management and education perspective

Wim Lambrechts
2019 The Central European Review of Economics and Management  
It highlights the attention given to, and the differences in interpretations of, 21st century skills, individual competences, personal capabilities and mind-sets related to sustainability, specifically  ...  competences, personal capabilities and mind-sets related to sustainability.  ...  Persuasion Persuading others to change their minds or behavior.  ... 
doi:10.29015/cerem.855 fatcat:qxtq5ddtizdmzgej3nas3fgemy

Shrines in Africa: History, Politics, and Society

Timothy Clack
2011 Time & Mind  
Acknowledgement: We acknowledge the wording around open access used by Australian publisher, re.press, and thank them for giving us permission to adapt their wording to our policy http://www.re-press.org  ...  S I R A K Before there were pots, the ancestors 'resided' in stones and when beer was offered to them it just rolled off their backs.  ...  The association of markets with pilgrimages is obviously synergistic, and in the fall, when farmers and tribesmen have paid off their debts and have a bit of disposable cash on them, the busses make their  ... 
doi:10.2752/175169711x12961583765531 fatcat:t7uwvgrxjveedcb3tcmsyc2nfq

From Supervenience to Superdupervenience: Meeting the Demands of a Material World

TERENCE HORGAN
1993 Mind  
Rene DePlanque of Fachinformationszentrum Chemie, Berlin, for providing us with the reaction data set used in evaluating the hierarchical classification algorithm.  ...  He decided to work on these books in any spare time and on weekends at the university. This would keep his mind off the "silly mis take" .  ...  His state of mind, when he searched in his memory, was similar to the time when he lost a beautiful girl friend when he was a študent.  ... 
doi:10.1093/mind/102.408.555 fatcat:aqjmty7ylvhxdeiuhihi2un7r4

A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity [article]

Pablo Hernandez-Leal, Michael Kaisers, Tim Baarslag, Enrique Munoz de Cote
2019 arXiv   pre-print
The key challenge in multiagent learning is learning a best response to the behaviour of other agents, which may be non-stationary: if the other agents adapt their strategy as well, the learning target  ...  Finally, we discuss in which environments the different approaches yield most merit, and point to promising avenues of future research.  ...  However, a large drawback is that most of them lose their theoretical guarantees when used in non-stationary environments (e.g., Q-learning).  ... 
arXiv:1707.09183v2 fatcat:mnducjpn7zawpnw3u6wnhhc6k4

Towards Continual Reinforcement Learning: A Review and Perspectives [article]

Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup
2020 arXiv   pre-print
In this article, we aim to provide a literature review of different formulations and approaches to continual reinforcement learning (RL), also known as lifelong or non-stationary RL.  ...  We then provide a taxonomy of different continual RL formulations and mathematically characterize the non-stationary dynamics of each setting.  ...  This work originated as a class project undertaken in the graduate-level course on Continual Learning: Towards "Broad" AI (IFT-6760B) at Mila, Montreal.  ... 
arXiv:2012.13490v1 fatcat:vcleqjnpgrbkvg477d4prmzg2q

Multiagent Deep Reinforcement Learning: Challenges and Directions Towards Human-Like Approaches [article]

Annie Wong, Thomas Bäck, Anna V. Kononova, Aske Plaat
2021 arXiv   pre-print
The combination of deep neural networks with reinforcement learning has gained increased traction in recent years and is slowly shifting the focus from single-agent to multiagent environments.  ...  We present the most common multiagent problem representations and their main challenges, and identify five research areas that address one or more of these challenges: centralised training and decentralised  ...  As the environment becomes non-stationary, each agent faces the moving-target problem: the best policy changes as the other agents' policies change (Busoniu et al., 2008; Papoudakis et al., 2019) .  ... 
arXiv:2106.15691v1 fatcat:7sy6cianq5dh5a7n6clvjdlrxy

Go-along interviewing with LGBTQ youth in Canada and the United States

Carolyn M. Porta, Heather L. Corliss, Jennifer M. Wolowic, Abigail Z. Johnson, Katie Fritz Fogel, Amy L. Gower, Elizabeth M. Saewyc, Marla E. Eisenberg
2017 Journal of LGBT Youth  
Go-along interviews, which are interviews conducted whilst being in and moving within participant selected spaces, were conducted with 66 LGBTQ adolescents (14-19 years old) in their self-identified communities  ...  Youth chose to walk, use public transportation, and drive to community locations, identifying numerous formal and informal resources in their communities.  ...  Beyond descriptive projects, go-along interview methods could be valuable tools in evaluation research, such as examining the benefits or outcomes of programmatic efforts, policy changes, or intervention  ... 
doi:10.1080/19361653.2016.1256245 pmid:28943992 pmcid:PMC5603221 fatcat:nv2jv5fxdzepxcd37jv32io6se

Horizon: Facebook's Open Source Applied Reinforcement Learning Platform [article]

Jason Gauci, Edoardo Conti, Yitao Liang, Kittipat Virochsiri, Yuchen He, Zachary Kaden, Vivek Narayanan, Xiaohui Ye, Zhengxing Chen, Scott Fujimoto
2019 arXiv   pre-print
The platform contains workflows to train popular deep RL algorithms and includes data preprocessing, feature transformation, distributed training, counterfactual policy evaluation, optimized serving, and  ...  In this paper we present Horizon, Facebook's open source applied reinforcement learning (RL) platform.  ...  Typically, the initial RL policy is trained on off-policy data generated by a non-RL production policy.  ... 
arXiv:1811.00260v5 fatcat:dq5kkotuqjfvxilhda2q7ebpgi

Reinforcement Learning of User Preferences for a Ubiquitous Personal Assistant [chapter]

Sofia Zaidenberg, Patrick Reignier
2011 Advances in Reinforcement Learning  
It is stationary because: -the environment without the user can be considered as stationary (the rules of evolution are not changing); -the end user might introduce a non stationary aspect (his behavior  ...  is evolving through time) but this non stationary part is embedded in the non observable part.  ... 
doi:10.5772/13723 fatcat:zdophkd7fnfilmq2uwsiyflyjy

Reinforcement Learning in Practice: Opportunities and Challenges [article]

Yuxi Li
2022 arXiv   pre-print
Then we discuss challenges, in particular, 1) foundation, 2) representation, 3) reward, 4) exploration, 5) model, simulation, planning, and benchmarks, 6) off-policy/offline learning, 7) learning to learn  ...  We conclude with a discussion, attempting to answer: "Why has RL not been widely adopted in practice yet?" and "When is RL helpful?".  ...  The agent needs to exploit the currently best action to maximize rewards greedily, yet it has to explore the environment to find better actions, when the policy is not optimal yet, or the system is non-stationary  ... 
arXiv:2202.11296v2 fatcat:xdtsmme22rfpfn6rgfotcspnhy

How Robot Verbal Feedback Can Improve Team Performance in Human-Robot Task Collaborations

Aaron St. Clair, Maja Mataric
2015 Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction - HRI '15  
It consists of a dynamic, synthetic task implemented in an augmented reality environment.  ...  The results demonstrate that the approach is capable of improving both objective measures of team performance and the user's subjective evaluation of both the task and the robot as a teammate.  ...  proof of concept designed to evaluate the approach in a co-located human-robot task collaboration as compared to a non-communicating robot.  ... 
doi:10.1145/2696454.2696491 dblp:conf/hri/ClairM15 fatcat:pbm3fbz7qbh4zfptkxa3bv6ft4

Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward [article]

Baihan Lin
2020 arXiv   pre-print
Our experiments on a variety of datasets, both in stationary and nonstationary environments of six different scenarios, demonstrated clear advantages of the proposed approach over the standard contextual  ...  semi-supervised learning setting, we introduced Background Episodic Reward LinUCB (BerlinUCB), a solution that easily incorporates clustering as a self-supervision module to provide useful side information when  ...  Conclusion We introduced an extension of the contextual bandit problem, learning from episodically revealed reward, motivated by several real-world applications in non-stationary environments, including  ... 
arXiv:2009.08457v2 fatcat:vhvcravl2fg7dmyjoptfwsrnmu
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