Toward automatic motivator selection for autism behavior intervention therapy.

Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interventions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to address the problem of selecting the right motivator for children with ASD using reinforcement learning by adapting to the most influential factors impacting the effectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov decision process problem. The states, actions and rewards design consider the factors that impact the effectiveness of a motivator based on applied behavior analysis as well as learners' individual preferences. We use a Q-learning algorithm to solve the modeled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.

User Acceptance Level of and Attitudes towards Telemedicine in the United Arab Emirates: A quantitative study.

OBJECTIVES: This study aimed to examine acceptance levels of and attitudes towards telemedicine among users in the United Arab Emirates (UAE) and assess associations between perceived usefulness (PU), perceived ease of use (PEOU), attitudes towards use (ATU) and behavioural intention of use (BIU) in relation to telemedicine technology. METHODS: This cross-sectional study used a simple random sampling design to obtain an appropriate sample from throughout the UAE. The technology acceptance model (TAM) and Rogers' diffusion of innovations (DOI) theory were applied as the conceptual basis for this study. An Arabic/English version of the questionnaire was distributed via email to physicians and nurses, members of the public (including patients), healthcare directors and information technology professionals. Data were collected from 1st March to 30th August 2019 and analysed using Statistical Package for the Social Sciences (SPSS). RESULTS: A total of 330 participants were included (response rate: 85.7%). BIU and PU were considered important elements of TAM in the adoption of telemedicine services compared to the other variables. The association between PEOU (beta = 0.033, P = 0.692), PU (beta = 0.034; P = 0.679) and ATU (beta = 0.055; P = 0.421) in relation to telemedicine were positive but not significant. However, BIU was found to be a strong significant predictor of actual usage (beta = 0.224; P = 0.003). CONCLUSION: This study confirms TAM's applicability in the adoption of telemedicine services in the UAE. The results show that users' perceptions were significantly related to their behavioural intention to use telemedicine. Factors influencing telemedicine technology are likely to vary as technology acceptance in other geographical areas may differ from the sample presented here.

Cooperating with machines.

Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human-machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions. Here, we develop an algorithm that combines a state-of-the-art reinforcement-learning algorithm with mechanisms for signaling. We show that this algorithm can cooperate with people and other algorithms at levels that rival human cooperation in a variety of two-player repeated stochastic games. These results indicate that general human-machine cooperation is achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms.

Corruption drives the emergence of civil society.

Centralized sanctioning institutions have been shown to emerge naturally through social learning, displace all other forms of punishment and lead to stable cooperation. However, this result provokes a number of questions. If centralized sanctioning is so successful, then why do many highly authoritarian states suffer from low levels of cooperation? Why do states with high levels of public good provision tend to rely more on citizen-driven peer punishment? Here, we consider how corruption influences the evolution of cooperation and punishment. Our model shows that the effectiveness of centralized punishment in promoting cooperation breaks down when some actors in the model are allowed to bribe centralized authorities. Counterintuitively, a weaker centralized authority is actually more effective because it allows peer punishment to restore cooperation in the presence of corruption. Our results provide an evolutionary rationale for why public goods provision rarely flourishes in polities that rely only on strong centralized institutions. Instead, cooperation requires both decentralized and centralized enforcement. These results help to explain why citizen participation is a fundamental necessity for policing the commons.

Behavioral experiments for assessing the abstract argumentation semantics of reinstatement.

Argumentation is a very fertile area of research in Artificial Intelligence, and various semantics have been developed to predict when an argument can be accepted, depending on the abstract structure of its defeaters and defenders. When these semantics make conflicting predictions, theoretical arbitration typically relies on ad hoc examples and normative intuition about what prediction ought to be the correct one. We advocate a complementary, descriptive-experimental method, based on the collection of behavioral data about the way human reasoners handle these critical cases. We report two studies applying this method to the case of reinstatement (both in its simple and floating forms). Results speak for the cognitive plausibility of reinstatement and yet show that it does not yield the full expected recovery of the attacked argument. Furthermore, results show that floating reinstatement yields comparable effects to that of simple reinstatement, thus arguing in favor of preferred argumentation semantics, rather than grounded argumentation semantics. Besides their theoretical value for validating and inspiring argumentation semantics, these results have applied value for developing artificial agents meant to argue with human users.