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’.

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.

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.

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)

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)

The concept of ‘human dignity’ sits at the heart of international human rights law and a growing number of national constitutions and yet its meaning is heavily contested and contingent. I aim to supplement the theoretical literature on dignity by providing an empirical study of how the concept is used in the specific context of legal discourse on sex work. I will analyse jurisprudence in which commercial sex was declared as incompatible with human dignity, focussing on the South African Constitutional (...) Court case of S v Jordan and the Indian Supreme Court case of Budhadev Karmaskar v State of West Bengal. I will consider how these courts conceptualise dignity and argue that their conclusions on the undignified nature of sex work are predicated on particular sexual norms that privilege emotional and relational intimacy. In light of the stigma faced by sex workers I will explore how a discourse, proclaiming sex work as beneath human dignity, may impact on the way that sex workers are perceived and represented culturally, arguing that it reinforces stigma. I will go on to examine how sex workers subvert the notion that commercial sex is undignified, and resist stigma, by campaigning for the right to sell sex with dignity. I will demonstrate that an alternative legal approach to dignity and sex work is possible, where the two are not considered as inherently incompatible, concluding with thoughts on the risks and benefits of using ‘dignity talk’ in activism and campaigns for sex work law reform. (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.

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)

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)

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 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.

Identity politics has been critiqued in various ways. One central problem—the Reinforcement Problem—claims that identity politics reinforces groups rooted in oppression thereby undermining its own liberatory aims. Here I consider two versions of the problem—one psychological and one metaphysical. I defang the first by drawing on work in social psychology. I then argue that careful consideration of the metaphysics of social groups and of the practice of identity politics provides resources to dissolve the second version. Identity politics involves the creation (...) or transformation of groups in ways that do not succumb to the metaphysical Reinforcement Problem. (shrink)