Shared intention normally leads to joint action. It does this, it is commonly said, only because it is a characteristically stable phenomenon, a phenomenon that tends to persist from the time it is formed until the time it is fulfilled. However, the issue of what the stability of shared intention comes down to remains largely undertheorized. My aim in this paper is to remedy this shortcoming. I argue that shared intention is a source of moral and epistemic reasons, that responsiveness (...) to such reasons on the part of each individual reinforces her own relevant attitudes, and that this enhances the stability of the shared intention as a whole. We thus discover an important self-reinforcing and stability-enhancing mechanism at the heart of shared intention and action. It follows from this proposal that the psychological commitment that an individual exhibits in joint action is normally a function not only of her own participatory intention, as often maintained, but also of her endorsement of the reasons created by her group’s shared intention to so act. (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)

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’.

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)

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)

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.

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.

Behaving ethically depends on the ability to recognize that ethical issues exist, to see from an ethical point of view. This ability to see and respond ethically may be related more to attributes of corporate culture than to attributes of individual employees. Efforts to increase ethical standards and decrease pressure to behave unethically should therefore concentrate on the organization and its culture. The purpose of this paper is to discuss how total quality (TQ) techniques can facilitate the development of a (...) cooperative corporate culture that promotes and encourages ethical behavior throughout an organization. (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.

In this paper I examine how the constituent elements of a firm's organizational structure affect the ethical behavior of workers. The formal features of organizations I examine are the compensation practices, performance and evaluation systems, and decision-making assignments. I argue that the formal organizational structure, which is distinguished from corporate culture, is necessary, though not sufficient, in solving ethical problems within firms. At best the formal structure should not undermine the ethical actions of workers. When combined with a strong culture, (...) however, the organizational structure may be sufficient in promoting ethical conduct. While helpful, ethics training and corporate codes are neither necessary nor sufficient in promoting ethical behavior within firms. (shrink)

Relational database management system is the most popular database system. It is important to maintain data security from information leakage and data corruption. RDBMS can be attacked by an outsider or an insider. It is difficult to detect an insider attack because its patterns are constantly changing and evolving. In this paper, we propose an adaptive database intrusion detection system that can be resistant to potential insider misuse using evolutionary reinforcement learning, which combines reinforcement learning and evolutionary learning. (...) The model consists of two neural networks, an evaluation network and an action network. The action network detects the intrusion, and the evaluation network provides feedback to the detection of the action network. Evolutionary learning is effective for dynamic patterns and atypical patterns, and reinforcement learning enables online learning. Experimental results show that the performance for detecting abnormal queries improves as the proposed model learns the intrusion adaptively using Transaction Processing performance Council-E scenario-based virtual query data. The proposed method achieves the highest performance at 94.86%, and we demonstrate the usefulness of the proposed method by performing 5-fold cross-validation. (shrink)

A deep reinforcement learning-based computational guidance method is presented, which is used to identify and resolve the problem of collision avoidance for a variable number of fixed-wing UAVs in limited airspace. The cooperative guidance process is first analyzed for multiple aircraft by formulating flight scenarios using multiagent Markov game theory and solving it by machine learning algorithm. Furthermore, a self-learning framework is established by using the actor-critic model, which is proposed to train collision avoidance decision-making neural networks. To achieve (...) higher scalability, the neural network is customized to incorporate long short-term memory networks, and a coordination strategy is given. Additionally, a simulator suitable for multiagent high-density route scene is designed for validation, in which all UAVs run the proposed algorithm onboard. Simulated experiment results from several case studies show that the real-time guidance algorithm can reduce the collision probability of multiple UAVs in flight effectively even with a large number of aircraft. (shrink)

Rachlin rightly highlights behavioural reinforcement, conditional cooperation, and framing. However, genes may explain part of the variance in altruistic behaviour. Framing cannot be used to support his theory of altruism. Reinforcement of acts is not identical to reinforcement of patterns of acts. Further, many patterns of acts could be reinforced, and Rachlin's altruism is not the most likely candidate.

Philosophers of science have offered different accounts of what it means for one scientific theory to reduce to another. I propose a more or less friendly amendment to Kenneth Schaffner’s “General Reduction-Replacement” model of scientific unification. Schaffner interprets scientific unification broadly in terms of a continuum from theory reduction to theory replacement. As such, his account leaves no place on its continuum for type irreducible and irreplaceable theories. The same is true for other accounts that incorporate Schaffner's continuum, for example, (...) those developed by Paul Churchland, Clifford Hooker, and John Bickle. Yet I believe a more general account of scientific unification should include type irreducible and irreplaceable theories in an account of their partial reduction, specifically, when there is a reduction of their tokens. Thus I propose a “Reduction-Reception-Replacement” model wherein type irreducible and irreplaceable theories are accepted or received for the purpose of unifying domains of particulars. I also suggest a link between this kind of token reduction and mechanistic explanation. (shrink)

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)