This commentary considers factors that may account for the inconsistency between the behavioral momentum formulation and the partial reinforcement extinction effect. The method of testing, the variability of the schedule, the nature of the response-contingency, and response effort are considered. Some applications to real-world problems are also discussed.

Many philosophers are building a solid case in favour of the knowledge account of assertion (KAA). According to KAA, if one asserts that P one represents oneself as knowing that P. KAA has recently received support from linguistic data about prompting challenges, parenthetical positioning and predictions. In this article, I add another argument to this rapidly growing list: an argument from what I will call ‘reinforcing parenthesis’.

The da’wah [invitation to Islamic teachings] movement of Ahl al-Sunnah wa al-Jama’ah al-Nahdhiyah, abbreviated as ASWAJA al-Nahdhiyah, formerly centred around elderly, rural, and traditional populations, has now expanded its influence to encompass the millennial demographic. The evolving landscape of time and technological advancements present novel challenges in effectively communicating the da’wah message to a generation deeply immersed in the digital era. Millennials exhibit distinct communication preferences and characteristics compared to previous generations, necessitating tailored approaches to disseminate da’wah content that resonates (...) with their context and needs. This research aimed to achieve two primary objectives: firstly, to identify emerging forms of new traditions within the ideological reinforcement of ASWAJA al-Nahdhiyah among millennials, and secondly, to analyse the underlying factors contributing to these novel traditions within the millennial context. Employing a qualitative approach, this study utilised nethnography as its methodological framework, seeking to understand cultural experiences encompassing traces, practices, networks, and social media systems. By combining various research approaches for data collection, analysis, and interpretation, the study shed light on the reinforcement efforts that give rise to diverse new traditions. The findings highlighted that the reinforcement endeavours, including the appropriation of media platforms and the emphasis on education, cadre development, and intensive mentorship within millennial domains, significantly contribute to the acceptance and affiliation towards ASWAJA al-Nahdhiyah. Moreover, these efforts serve as a counterforce against the proliferation of radical ideologies, safeguarding the traditional religious views amid the rapid globalisation of religious understanding and transnational da’wah movements in Indonesia.Contribution: This article advanced our understanding of the evolving dynamics within the ASWAJA al-Nahdhiyah movement, particularly its adaptation to contemporary communication channels and its strategic engagement with young audiences amid the changing religious landscape in Indonesia. (shrink)

There is an apparent connection between reinforcement learning and agency. Artificial entities controlled by reinforcement learning algorithms are standardly referred to as agents, and the mainstream view in the psychology and neuroscience of agency is that humans and other animals are reinforcement learners. This article examines this connection, focusing on artificial reinforcement learning systems and assuming that there are various forms of agency. Artificial reinforcement learning systems satisfy plausible conditions for minimal agency, and those which (...) use models of the environment to perform forward search are capable of a form of agency which may reasonably be called action for reasons. (shrink)

Animals routinely adapt to changes in the environment in order to survive. Though reinforcement learning may play a role in such adaptation, it is not clear that it is the only mechanism involved, as it is not well suited to producing rapid, relatively immediate changes in strategies in response to environmental changes. This research proposes that counterfactual reasoning might be an additional mechanism that facilitates change detection. An experiment is conducted in which a task state changes over time and (...) the participants had to detect the changes in order to perform well and gain monetary rewards. A cognitive model is constructed that incorporates reinforcement learning with counterfactual reasoning to help quickly adjust the utility of task strategies in response to changes. The results show that the model can accurately explain human data and that counterfactual reasoning is key to reproducing the various effects observed in this change detection paradigm. (shrink)

We investigate how Reinforcement Learning methods can be used to solve the production selection and production ordering problem in ACT‐R. We focus on four algorithms from the Q learning family, tabular Q and three versions of Deep Q Networks, as well as the ACT‐R utility learning algorithm, which provides a baseline for the Q algorithms. We compare the performance of these five algorithms in a range of lexical decision tasks framed as sequential decision problems.

The concept of “intrinsic reinforcement” stretches the use of “reinforcement” beyond where it is valuable. The concept of the “self-system,” though fuzzy at the edges, can cover experience as well as the behaviour of altruistic acts.

Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense mission planning is described as a sequential decision-making problem and formalized as a Markov decision process. (...) Then, the SEAD intelligent planning model based on the proximal policy optimization algorithm is established and a general intelligent planning architecture is proposed. Furthermore, three policy training tricks, i.e., domain randomization, maximizing policy entropy, and underlying network parameter sharing, are introduced to improve the learning performance and generalizability of PPO. Experiments results show that the model in this work is efficient and stable, and can be adapted to the unknown continuous high-dimensional environment. It can be concluded that the UAV intelligent mission planning model based on DRL has powerful intelligent planning performance, and provides a new idea for researching UAV autonomy. (shrink)

In this opinionated review, I draw attention to some of the contributions reinforcement learning can make to questions in the philosophy of mind. In particular, I highlight reinforcement learning's foundational emphasis on the role of reward in agent learning, and canvass two ways in which the framework may advance our understanding of perception and motivation.

I argue for the role of reinforcement learning in the philosophy of mind. To start, I make several assumptions about the nature of reinforcement learning and its instantiation in minds like ours. I then review some of the contributions of reinforcement learning methods have made across the so-called 'decision sciences.' Finally, I show how principles from reinforcement learning can shape philosophical debates regarding the nature of perception and characterisations of desire.

The area of smart power grids needs to constantly improve its efficiency and resilience, to provide high quality electrical power in a resilient grid, while managing faults and avoiding failures. Achieving this requires high component reliability, adequate maintenance, and a studied failure occurrence. Correct system operation involves those activities and novel methodologies to detect, classify, and isolate faults and failures and model and simulate processes with predictive algorithms and analytics. In this paper, we showcase the application of a complex-adaptive, self-organizing (...) modeling method, and Probabilistic Boolean Networks, as a way towards the understanding of the dynamics of smart grid devices, and to model and characterize their behavior. This work demonstrates that PBNs are equivalent to the standard Reinforcement Learning Cycle, in which the agent/model has an interaction with its environment and receives feedback from it in the form of a reward signal. Different reward structures were created to characterize preferred behavior. This information can be used to guide the PBN to avoid fault conditions and failures. (shrink)

A fundamental question in reading research concerns whether attention is allocated strictly serially, supporting lexical processing of one word at a time, or in parallel, supporting concurrent lexical processing of two or more words (Reichle, Liversedge, Pollatsek, & Rayner, 2009). The origins of this debate are reviewed. We then report three simulations to address this question using artificial reading agents (Liu & Reichle, 2010; Reichle & Laurent, 2006) that learn to dynamically allocate attention to 1–4 words to “read” as efficiently (...) as possible. These simulation results indicate that the agents strongly preferred serial word processing, although they occasionally attended to more than one word concurrently. The reason for this preference is discussed, along with implications for the debate about how humans allocate attention during reading. (shrink)