Of the approximately 3.9 million dogs that enter US animal shelters each year, many exhibit behaviors related to fear, which can affect their likelihood of adoption. Current dog training procedures to treat fear include counterconditioning and desensitization, which can often take months or years to show any behavior change and do not teach specific behaviors aimed to increase the dog's chance of being adopted. The current study used a negative reinforcement shaping procedure to teach fearful dogs to approach and and interact with people. The results showed that constructional fear treatment increased the amount of time the dog spent at the front of the kennel, and increased sniffing, tail wagging, and accepting petting for all 3 participants.

We consider a class of sequential decision making problems in the presence of uncertainty, which belongs to the field of Reinforcement Learning (RL). Specifically, we study discrete Markov decision Processes (MDPs) which model a decision maker or agent that interacts with a stochastic and dynamic environment and receives feedback from it in the form of a reward. The agent seeks to maximize a notion of cumulative reward. Because the environment (both the system dynamics and reward function) is unknown, it faces an exploration-exploitation dilemma, where it must balance exploring its available actions or exploiting what it believes to be the best one. This dilemma captured by the notion of regret, which compares the rewards that the agent has accumulated thus far with those that would have been obtained by an optimal policy. The agent is then said to behave optimally, if it minimizes its regret. This thesis investigates the fundamental regret limits that can be achieved by any agent. We derive general asymptotic and problem specific regret lower bounds for the cases of ergodic and deterministic MDPs. We make these explicit for ergodic MDPs that are unstructured, for MDPs with Lipschitz transitions and rewards, as well as for deterministic MDPs that satisfy a decoupling property. Furthermore, we propose DEL, an algorithm that is valid for any ergodic MDP with any structure and whose regret upper bound matches the associated regret lower bounds, thus being truly optimal. For this algorithm, we present theoretical regret guarantees as well as a numerical demonstration that verifies its ability to exploit the underlying structure. / <p>QC 20210603</p>

No description available.

No description available.

No description available.

When discriminated IRT procedures have been used to determine preference relations among temporally extended operants, deviations from predictions of the matching law have been found (Hawkes and Shimp, 1974). Using a yoked-control procedure, the present study shows that keypecking in the discriminated IRT procedure has two sources of strength, that arising from the stimulus-reinforcer contingency and that arising from the response-reinforcer contingency Three out of four yoked birds autoshaped to the keylight, and all lead birds showed evidence of control by the keylight under some conditions. As any control of keypecking by the keylight, either discriminated or autoshaped, contributes to deviations from matching, the discriminated IRT procedure does not permit one to draw strong conclusions about preference relations among IRTs.

No description available.

No description available.

<p> Experiments were conducted, with rats, to ascertain the effects of partial reinforcement in aversive classical conditioning. Conditioned suppression of bar-pressing was more resistant to extinction following intermittent reinforcement of a conditioned stimulus than following consistent reinforcement. This effect was obtained whether or not bar-pressing was permitted during conditioning as well as during extinction. The effect was amplified by interpolating a large block of nonreinforced trials early in the partial schedule; it was eliminated by adding more reinforced trials prior to the partial schedule. The effect was not obtained by interpolating a large block of nonreinforcements in a continuous schedule. The data were related to current theoretical conceptions of partial reinforcement.</p> / Thesis / Doctor of Philosophy (PhD)

Cognitive radar networks (CRNs) were first proposed in 2006 by Simon Haykin, shortly after the introduction of cognitive radar. In order for CRNs to benefit from many of the optimization techniques developed for cognitive radar, they must have some method of coordination and control. Both centralized and distributed architectures have been proposed, and both have drawbacks. This work addresses gaps in the literature by providing the first consideration of the problems that appear when typical cognitive radar tools are extended into networks. This work first examines the online learning techniques available to distributed CRNs, enabling optimal resource allocation without requiring a dedicated communication resource. While this problem has been addressed for single-node cognitive radar, we provide the first consideration of mutual interference in such networks. We go on to propose the first hybrid cognitive radar network structure which takes advantage of central feedback while maintaining the benefits of distributed networks. Then, we go on to investigate a novel problem of timely updating in CRNs, addressing questions of target update frequency and node updating methods. We draw from the Age of Information literature to propose Bellman-optimal solutions. Finally, we introduce the notion of mode control, and develop a way to select between active and passive target observation. / Doctor of Philosophy / Cognitive radar was inspired by biological models, where animals such as dolphins or bats use vocal pulses to form a model of their environment. As these animals seek after prey, they use information they observe to modify their vocal pulses. Cognitive radar networks are an extension of this model to a group of radar devices, which must work together cooperatively to detect and track targets. As the scene changes in time, the radar nodes in the cognitive radar network must change their operating parameters to continue performing well. This networked problem has issues not present in the single-node cognitive radar problem. In particular, as each node in the network changes operating parameters, it risks degrading the performance of the other nodes. In the contribution of this dissertation, we investigate the techniques that a cognitive radar network can use to avoid these cases of mutual performance degradation, and in particular, we investigate how this can be done without advance coordination between the nodes. In the second contribution, we go on to explore what performance improvements are available as central control is introduced. The third and fourth contributions investigate further efficiencies available to a cognitive radar network. The third contribution discusses how a resource-constrained network should communicate updates to a central aggregator. Lastly, the fourth contribution investigates additional estimation tools available to such a network, and how the network should choose between these modes.