scholarly journals Towards Automatic Motivator Selection for Autism Behavior Intervention Therapy

2021 ◽  
Author(s):  
Nur Siyam ◽  
Sherief Abdallah
Keyword(s):  
Learning Algorithm ◽  
Autism Spectrum ◽  
Mobile App ◽  
Influential Factors ◽  
Algorithm Performance ◽  
Q Learning ◽  
Academic Activities ◽  
Markov Decision ◽  
The Right ◽  
The Impact

Abstract 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 solve 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 modelled 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.

scholarly journals Review of Cognitive Characteristics of Autism Spectrum Disorder Using Performance on Six Subtests on Four Versions of the Wechsler Intelligence Scale for Children

2021 ◽  
Author(s):  
Mizuho Takayanagi ◽  
Yoko Kawasaki ◽  
Mieko Shinomiya ◽  
Hoshino Hiroshi ◽  
Satoshi Okada ◽  
...  
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Block Design ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Intelligence Scale ◽  
Cognitive Characteristics ◽  
Children With Asd ◽  
The Right ◽  
The Impact

AbstractThis study was a systematic review of research using the Wechsler Intelligence Scale for Children (WISC) with Autism Spectrum Disorder (ASD) to examine cognitive characteristics of children with ASD beyond the impact of revisions based on WISC and diagnostic criteria changes. The classic “islets of ability” was found in individuals with full-scale IQs < 100. The “right-descending profiles” were observed among high IQ score individuals. High levels on the Block Design and low Coding levels were consistently found regardless of the variation in intellectual functioning or diagnosis. This review identified patterns of cognitive characteristics in ASD individuals using empirical data that researchers may have previously been aware of, based on their experiences, owing to the increased prevalence of ASD.

The emerging threat of artificial intelligence on competition in liberalized electricity markets: A deep Q-network approach

2021 ◽  
Author(s):  
Danial Esmaeili Aliabadi ◽  
Katrina Chan
Keyword(s):  
Electricity Markets ◽  
Information Disclosure ◽  
End Users ◽  
Influential Factors ◽  
Bidding Strategies ◽  
Q Learning ◽  
Development Goals ◽  
Available Information ◽  
The Impact

Abstract BackgroundAccording to sustainable development goals (SDGs), societies should have access to affordable, reliable, and sustainable energy. Deregulated electricity markets have been established to provide affordable electricity for end-users through advertising competition. Although these liberalized markets are expected to serve this purpose, they are far from perfect and are prone to threats, such as collusion. Tacit collusion is a condition, in which power generating companies (GenCos) disrupt the competition by exploiting their market power. MethodsIn this manuscript, a novel deep Q-network (DQN) model is developed, which GenCos can use to determine the bidding strategies to maximize average long-term payoffs using available information. In the presence of collusive equilibria, the results are compared with a conventional Q-learning model that solely relies on past outcomes. With that, this manuscript aims to investigate the impact of emerging DQN models on the establishment of collusive equilibrium in markets with repetitive interactions among players. Results and ConclusionsThe outcomes show that GenCos may be able to collude unintentionally while trying to ameliorate long-term profits. Collusive strategies can lead to exorbitant electric bills for end-users, which is one of the influential factors in energy poverty. Thus, policymakers and market designers should be vigilant regarding the combined effect of information disclosure and autonomous pricing, as new models exploit information more effectively.

scholarly journals Visual Tracking Using Wang–Landau Reinforcement Sampler

2020 ◽  
Vol 10 (21) ◽  
pp. 7780
Author(s):  
Dokyeong Kwon ◽  
Junseok Kwon
Keyword(s):  
Visual Tracking ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Tracking System ◽  
Network Architectures ◽  
Tracking Accuracy ◽  
Algorithm Performance ◽  
Q Learning ◽  
Number Of Visits

In this study, we present a novel tracking system, in which the tracking accuracy can be considerably enhanced by state prediction. Accordingly, we present a new Q-learning-based reinforcement method, augmented by Wang–Landau sampling. In the proposed method, reinforcement learning is used to predict a target configuration for the subsequent frame, while Wang–Landau sampler balances the exploitation and exploration degrees of the prediction. Our method can adapt to control the randomness of policy, using statistics on the number of visits in a particular state. Thus, our method considerably enhances conventional Q-learning algorithm performance, which also enhances visual tracking performance. Numerical results demonstrate that our method substantially outperforms other state-of-the-art visual trackers and runs in realtime because our method contains no complicated deep neural network architectures.

scholarly journals Implementation of modified Q learning technique in EMCAP control architecture

2017 ◽  
Vol 7 (1.5) ◽  
pp. 269
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Learning Algorithm ◽  
Control Strategies ◽  
Cognitive Architecture ◽  
Dynamic Environment ◽  
Learning System ◽  
Selection Strategy ◽  
Q Learning ◽  
Learning Techniques ◽  
Markov Decision ◽  
Environment Experiment

This research introduces a self learning modified (Q-Learning) techniques in a EMCAP (Enhanced Mind Cognitive Architecture of pupils). Q-learning is a modelless reinforcement learning (RL) methodology technique. In Specific, Q-learning can be applied to establish an optimal action-selection strategy for any respective Markov decision process. In this research introduces the modified Q-learning in a EMCAP (Enhanced Mind Cognitive Architecture of pupils). EMCAP architecture [1] enables and presents various agent control strategies for static and dynamic environment.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance.his modified q learning algorithm can be more suitable in EMCAP architecture.  The experiments are conducted the modified Q-Learning system gets more rewards compare to existing Q-learning.

scholarly journals Does Personalized Nutrition Increase Weight Loss Self-Efficacy?

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 1310-1310
Author(s):  
Lu Hu ◽  
Chan Wang ◽  
Huilin Li ◽  
Margaret Curran ◽  
Collin J Popp ◽  
...  
Keyword(s):  
Weight Loss ◽  
Real Time ◽  
Self Efficacy ◽  
Learning Algorithm ◽  
Mobile App ◽  
Small Sample ◽  
Dietary Counseling ◽  
Macronutrient Distribution ◽  
The Impact

Abstract Objectives We examined whether a diet personalized to reduce postprandial glycemic response (PPGR) to foods increases weight loss self-efficacy. Methods The Personal Diet Study is an ongoing clinical trial that aims to compare two weight loss diets: a one-size-fits-all, calorie-restricted, low-fat diet (Standardized) versus a diet having the same calorie restriction but utilizing a machine learning algorithm to predict and reduce PPGR (Personalized). Both groups receive the same behavioral counseling to enhance weight loss self-efficacy. Both groups self-monitor dietary intake using a mobile app, with Standardized receiving real-time feedback on calories and macronutrient distribution, and Personalized receiving real time feedback on calories, macronutrient distribution, and predicted PPGR. We examined changes in self-efficacy between baseline and 3 mos, using the 20-item Weight Efficacy Lifestyle questionnaire (WEL). Linear mixed models were used to analyze differences, adjusting for age, gender, and race. Results The analyses included the first 75 participants to complete 3-mos assessments (41 Personalized and 34 Standardized). The majority of the participants were white (69.3%), female (61.3%), with a mean age of 61.7 years (SD = 9.9) and BMI of 33.4 kg/m2 (SD = 4.8). At baseline, WEL scores were similar between the 2 groups [Standardized WEL: 118.8 (SD = 27.6); Personalized WEL: 124.9 (SD = 29.5), P = 0.47]. At 3 mos, the WEL score was significantly improved in both groups [16.0 (SD = 4.1) in the Standardized group (P &lt; 0.001) and 7.4 (SD = 3.7) in the Personalized group (P = 0.048)], but the between group difference was not significant (P = 0.12). Conclusions Personalized feedback on predicted PPGRs does not appear to enhance weight loss self-efficacy at 3 mos. The lack of significance may be related to the short follow-up period in these preliminary analyses, the small sample accrued to date, or the fact that WEL is designed to assess confidence in various situations (e.g., depressed, anxious) that may not be impacted by personalization. These analyses will be replicated with a larger sample using data obtained through the 6-mos follow-up. New self-efficacy measures may be required to assess the impact of personalized dietary counseling. Funding Sources This research was supported by the American Heart Association.

Reinforcement Learning Applied to a Differential Game

1995 ◽  
Vol 4 (1) ◽  
pp. 3-28 ◽  
Author(s):  
Mance E. Harmon ◽  
Leemon C. Baird ◽  
A. Harry Klopf
Keyword(s):  
Reinforcement Learning ◽  
Differential Game ◽  
Learning Algorithm ◽  
Learning System ◽  
Test Bed ◽  
Linear Quadratic ◽  
Time Step ◽  
Q Learning ◽  
Step Duration ◽  
Markov Decision

An application of reinforcement learning to a linear-quadratic, differential game is presented. The reinforcement learning system uses a recently developed algorithm, the residual-gradient form of advantage updating. The game is a Markov decision process with continuous time, states, and actions, linear dynamics, and a quadratic cost function. The game consists of two players, a missile and a plane; the missile pursues the plane and the plane evades the missile. Although a missile and plane scenario was the chosen test bed, the reinforcement learning approach presented here is equally applicable to biologically based systems, such as a predator pursuing prey. The reinforcement learning algorithm for optimal control is modified for differential games to find the minimax point rather than the maximum. Simulation results are compared to the analytical solution, demonstrating that the simulated reinforcement learning system converges to the optimal answer. The performance of both the residual-gradient and non-residual-gradient forms of advantage updating and Q-learning are compared, demonstrating that advantage updating converges faster than Q-learning in all simulations. Advantage updating also is demonstrated to converge regardless of the time step duration; Q-learning is unable to converge as the time step duration grows small.

Jamming-Resilient Wideband Cognitive Radios with Multi-Agent Reinforcement Learning

2018 ◽  
Vol 10 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Mohamed A. Aref ◽  
Sudharman K. Jayaweera
Keyword(s):  
Learning Algorithm ◽  
Cognitive Radios ◽  
System Model ◽  
Interference Avoidance ◽  
Q Learning ◽  
Selection Policy ◽  
Cognitive Framework ◽  
Multi Agent ◽  
Simulation Results ◽  
The Impact

This article presents a design of a wideband autonomous cognitive radio (WACR) for anti-jamming and interference-avoidance. The proposed system model allows multiple WACRs to simultaneously operate over the same spectrum range producing a multi-agent environment. The objective of each radio is to predict and evade a dynamic jammer signal as well as avoiding transmissions of other WACRs. The proposed cognitive framework is made of two operations: sensing and transmission. Each operation is helped by its own learning algorithm based on Q-learning, but both will be experiencing the same RF environment. The simulation results indicate that the proposed cognitive anti-jamming technique has low computational complexity and significantly outperforms non-cognitive sub-band selection policy while being sufficiently robust against the impact of sensing errors.

scholarly journals Intelligent Stretch Optimization in Information Centric Networking-Based Tactile Internet Applications

2021 ◽  
Vol 11 (16) ◽  
pp. 7351
Author(s):  
Hussain Ahmad ◽  
Muhammad Zubair Islam ◽  
Rashid Ali ◽  
Amir Haider ◽  
Hyungseok Kim
Keyword(s):  
Learning Algorithm ◽  
Mobile Network ◽  
Optimal Routing ◽  
Information Centric Networking ◽  
Internet Applications ◽  
Q Learning ◽  
Communication Paradigm ◽  
Markov Decision ◽  
Data Content ◽  
Simulation Results

The fifth-generation (5G) mobile network services are currently being made available for different use case scenarios like enhanced mobile broadband, ultra-reliable and low latency communication, and massive machine-type communication. The ever-increasing data requests from the users have shifted the communication paradigm to be based on the type of the requested data content or the so-called information-centric networking (ICN). The ICN primarily aims to enhance the performance of the network infrastructure in terms of the stretch to opt for the best routing path. Reduction in stretch merely reduces the end-to-end (E2E) latency to ensure the requirements of the 5G-enabled tactile internet (TI) services. The foremost challenge tackled by the ICN-based system is to minimize the stretch while selecting an optimal routing path. Therefore, in this work, a reinforcement learning-based intelligent stretch optimization (ISO) strategy has been proposed to reduce stretch and obtain an optimal routing path in ICN-based systems for the realization of 5G-enabled TI services. A Q-learning algorithm is utilized to explore and exploit the different routing paths within the ICN infrastructure. The problem is designed as a Markov decision process and solved with the help of the Q-learning algorithm. The simulation results indicate that the proposed strategy finds the optimal routing path for the delay-sensitive haptic-driven services of 5G-enabled TI based upon their stretch profile over ICN, such as the augmented reality /virtual reality applications. Moreover, we compare and evaluate the simulation results of propsoed ISO strategy with random routing strategy and history aware routing protocol (HARP). The proposed ISO strategy reduces 33.33% and 33.69% delay as compared to random routing and HARP, respectively. Thus, the proposed strategy suggests an optimal routing path with lesser stretch to minimize the E2E latency.

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