Learning obstacle avoidance with an operant behavioral model

Artificial intelligence researchers have been attracted by the idea of having robots learn how to accomplish a task, rather than being told explicitly. Reinforcement learning has been proposed as an appealing framework to be used in controlling mobile agents. Robot learning research, as well as research in biological systems, face many similar problems in order to display high flexibility in performing a variety of tasks. In this work, the controlling of a vehicle in an avoidance task by a previously developed operant learning model (a form of animal learning) is studied. An environment in which a mobile robot with proximity sensors has to minimize the punishment for colliding against obstacles is simulated. The results were compared with the Q-Learning algorithm, and the proposed model had better performance. In this way a new artificial intelligence agent inspired by neurobiology, psychology, and ethology research is proposed.Fil: Gutnisky, D. A.. Universidad de Buenos Aires. Facultad de Ingeniería.Instituto de Ingeniería Biomédica; ArgentinaFil: Zanutto, Bonifacio Silvano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería.Instituto de Ingeniería Biomédica; Argentin

The Acute Effects of caffeine and L-theanine on Cognition in Older Adults

The acute effects of L-theanine and Caffeine on cognition in older adults Tilley, L., Gray, M., Stone, M., & Binns, A.; Human Performance Lab; University of Arkansas; Fayetteville, AR. Introduction: Cognitive decline is an impairment that affects many adults and has the potential to be decelerated. Previous studies show that caffeine and L-theanine positively affect cognition in a young population. L-theanine is an amino acid found in tea that produces a relaxation affect. Therefore, the purpose of this study is to determine the acute effects of caffeine and L-theanine on cognition in older adults. Methodology: Fifty-three older adults ages 55 and older participated in this study. Each adult completed the Mini Mental State Exam and participated in a health questionnaire. Both Trails Making Tests (Trails A and Trails B) and the Stroop Color-Word test were administered to measure cognition. Only the trails B and the last portion of the Stroop Color-Word test will be analyzed because they proposed the greatest challenge to the individual. This double-blind controlled trial randomly assigned the participants either 100 mg caffeine, 200 mg of L-theanine, or a placebo (microcrystalline cellulose). After a 60-minute wait period, all tests were repeated and compared to baseline measurements. Results: There was no statistically significant group by time interaction for the Trails B test (p = 0.389). However, there was a time effect interaction between pre-and post-measurements (p = 0.000) showing that time to complete the test decreased. The Stroop Color Word test also had no statistically significant group by time interaction (p = 0.632), but the time effect showed a significant difference (p = 0.001). Each group improved their testing time, but not one specific supplement statistically influenced that improvement. Discussion: Although not statistically significant, Trails B and the Stroop Color Word test showed a decrease in time to complete each test, suggesting that caffeine and L-theanine could affect cognition. The rate of absorbance decreases with age, therefore the wait period could have been longer than the studies where the age range was lower. For some studies that did obtain the results we expected, their dosage was higher and we chose a safe dosage previously administered on a younger population

Training a New Trick Using No-Reward Markers: Effects on Dogs’ Performance and Stress Behaviors

This study explored using no-reward markers (NRMs). Dogs were taught a novel trick. In the IG group dogs’ errors were ignored; in the NRM group they elicited a tone. Performance and stress were evaluated. IG dogs reached higher levels of performance, with no difference in the frequency of stress behaviors

Skinner operant conditioning model and robot bionic self-learning control

Fuzzy Skinner Operant Conditioning Automaton (FSOCA) sastavljen je na temelju Operant Conditioning mehanizma primjenom teorije neizrazitih skupova. Osnovno obilježje automata FSOCA je sljedeće: neizraziti rezultati stanja pomoću Gausove funkcije koriste se kao skupovi neizrazitog stanja; neizrazita pravila preslikavanja (fuzzy mapping rules) kod fuzzy-conditioning-operacije zamjenjuju stohastičke "conditioning-operant" skupove preslikavanja. Stoga se automat FSOCA može koristiti za opisivanje, simuliranje i dizajniranje raznih samo-organizirajućih radnji fuzzy nesigurnog sustava. Automat FSOCA najprije usvaja online algoritam grupiranja (clustering) u svrhu podjele ulaznog prostora (input space) te koristi intenzitet pobude pravila preslikavanja kako bi odlučio treba li generirati novo pravilo preslikavanja da bi broj pravila preslikavanja bio ekonomičan. Dizajnirani FSOCA automat primijenjen je za reguliranje balansiranja gibanja robota s dva kotača. Kako se učenje nastavlja, odabrana vjerojatnoća fuzzy operanta koji optimalno slijedi postepeno će se povećavati, entropijsko djelovanje fuzzy operanta će se postepeno smanjivati pa će se automatski generirati i izbrisati neizrazita pravila preslikavanja. Nakon otprilike sedamnaest krugova obuke, odabrane vjerojatnosti neizrazitog posljedičnog optimalnog operanta postupno teže prema jednoj, entropija djelovanja neizrazitog operanta postupno se smanjuje i broj neizrazitih pravila preslikavanja postaje optimalan. Tako robot postupno uči vještinu balansiranja gibanja.A Fuzzy Skinner Operant Conditioning Automaton (FSOCA) is constructed based on Operant Conditioning Mechanism with Fuzzy Set theory. The main character of FSOCA automaton is: the fuzzed results of state by Gaussian function are used as fuzzy state sets; the fuzzy mapping rules of fuzzy-conditioning-operation replace the stochastic "conditioning-operant" mapping sets. So the FSOCA automaton can be used to describe, simulate and design various self-organization actions of a fuzzy uncertain system. The FSOCA automaton firstly adopts online clustering algorithm to divide the input space and uses the excitation intensity of mapping rule to decide whether a new mapping rule needs to be generated in order to ensure that the number of mapping rules is economical. The designed FSOCA automaton is applied to motion balanced control of two-wheeled robot. With the learning proceeding, the selected probability of the optimal consequent fuzzy operant will gradually increase, the fuzzy operant action entropy will gradually decrease and the fuzzy mapping rules will automatically be generated and deleted. After about seventeen rounds of training, the selected probabilities of fuzzy consequent optimal operant gradually tend to one, the fuzzy operant action entropy gradually tends to minimum and the number of fuzzy mapping rules is optimum. So the robot gradually learns the motion balance skill

Incremental Robot Shaping

We propose a modular architecture for autonomous robots which allows for the implementation of basic behavioral modules by both programming and training, and accommodates for an evolutionary development of the interconnections among modules. This architecture can implement highly complex controllers and allows for incremental shaping of the robot behavior. Our proposal is exemplified and evaluated experimentally through a number of mobile robotic tasks involving exploration, battery recharging and object manipulation

Teachers’ Use of Diverse Praise: A Middle and High School Sample

The current study examined teachers’ use of diverse praise or the use of verbal statements or gestures of approval that are delivered in a variety of distinguishable ways in response to desired student behavior. Verbatim general praise and behavior-specific praise data collected during the 2017-18 academic year were analyzed from a larger study where a total of 1,320 observed minutes were collected across 66 middle and high school classrooms. Teachers used an average of 1.7 total diverse praise categories per observation. Both middle and high school teachers used more general diverse praise categories compared to behavior-specific diverse praise categories. The most commonly observed categories included the adjective (e.g., great; 68%), work (e.g., nice work, 18%), and compliance/appreciation (e.g., thank you, 18%) GDP categories. Overall, the only GDP categories coded included general praise that was delivered verbally. There was no statistically significant difference between middle and high school teachers’ use of diverse praise. When comparing overall middle school (sixth through eighth grade) and high school (ninth through twelfth grade) total diverse praise (TDP), general diverse praise (GDP), and behavior-specific diverse praise (BSDP) numbers were similar and the averages obtained from each category were relatively stable (i.e., without trend)

Developmental Robots - A New Paradigm

It has been proved to be extremely challenging for humans to program a robot to such a sufficient degree that it acts properly in a typical unknown human environment. This is especially true for a humanoid robot due to the very large number of redundant degrees of freedom and a large number of sensors that are required for a humanoid to work safely and effectively in the human environment. How can we address this fundamental problem? Motivated by human mental development from infancy to adulthood, we present a theory, an architecture, and some experimental results showing how to enable a robot to develop its mind automatically, through online, real time interactions with its environment. Humans mentally “raise” the robot through “robot sitting” and “robot schools” instead of task-specific robot programming

Teachers’ Use of Diverse Praise: A Middle and High School Sample

The current study examined teachers’ use of diverse praise or the use of verbal statements or gestures of approval that are delivered in a variety of distinguishable ways in response to desired student behavior. Verbatim general praise and behavior-specific praise data collected during the 2017-18 academic year were analyzed from a larger study where a total of 1,320 observed minutes were collected across 66 middle and high school classrooms. Teachers used an average of 1.7 total diverse praise categories per observation. Both middle and high school teachers used more general diverse praise categories compared to behavior-specific diverse praise categories. The most commonly observed categories included the adjective (e.g., great; 68%), work (e.g., nice work, 18%), and compliance/appreciation (e.g., thank you, 18%) GDP categories. Overall, the only GDP categories coded included general praise that was delivered verbally. There was no statistically significant difference between middle and high school teachers’ use of diverse praise. When comparing overall middle school (sixth through eighth grade) and high school (ninth through twelfth grade) total diverse praise (TDP), general diverse praise (GDP), and behavior-specific diverse praise (BSDP) numbers were similar and the averages obtained from each category were relatively stable (i.e., without trend)