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.

No description available.

No description available.

No description available.

This quasi-experimental field study examined the efficacy of an intervention to decrease alcohol abuse by college students. The harm reduction approach states that the ultimate goal when dealing with an unsafe behavior should be abstinence, but any change in behavior in the direction of less harm is supported. This approach was used as the basis of the current research, in combination with differential reinforcement in order to reduce alcohol consumption and its behavioral outcome, blood alcohol concentration (BAC). A total of 409 male and female college students participated while in the applied setting of four fraternity parties. The study took place at two separate fraternity houses, a control fraternity and an experimental fraternity. During the intervention phase, participants with a BAC below .05 were entered into a raffle to win a cash prize. Upon entry to the intervention party, participants were given flyers announcing the raffle and contingency, and gender-specific nomograms to aid in BAC self-monitoring. Dependent measures were blood alcohol concentration measured by hand-held breathalyzers, percentage of participants below criterion BAC levels (i.e., .05 and .08), accuracy of BAC self-estimation, number of negative outcomes due to excessive alcohol consumption, number of positive outcomes due to abstinence or moderate alcohol consumption, and amount of reported fun experienced at the party. Results showed the intervention did not significantly reduce the intoxication of participants or increase the percentage of participants below criterion BAC levels. These results are best explained by a floor effect, as the experimental fraternity had a relatively low baseline BAC. The use of nomograms at the intervention party increased the accuracy of students' BAC self-estimations. Implications for nomogram use and improvements for future implementation of the incentive/reward intervention are discussed. / Master of Science

This thesis investigates the computational principles underlying sensorimotor synchronization (SMS) through the novel application of deep reinforcement learning (RL). SMS, the coordination of rhythmic movement with external stimuli, is essential for human activities like music performance and social interaction, yet its neural mechanisms and learning processes are not fully understood. We present a computational framework utilizing recurrent neural networks with Long Short-Term Memory (LSTM) units, trained via RL, to model SMS behavior. This approach allows for the exploration of how different reward structures shape the acquisition and execution of synchronization skills. Our model is evaluated on both steady-state synchronization and perturbation response tasks, paralleling human SMS studies. Key findings reveal that agents trained with a combined reward—minimizing next-beat asynchrony and maintaining interval accuracy—exhibit human-like adaptive behaviors. Notably, these agents exhibited asymmetric error correction, making larger adjustments for late versus early taps, a phenomenon documented in human subjects. This suggests that such asymmetry may arise from the inherent reward structure of the task rather than from specific neural architectures. While our model did not consistently reproduce the negative mean asynchrony observed in human steady-state tapping, it demonstrated anticipatory behavior in response to perturbations. This offers new insights into how the brain might learn and execute rhythmic tasks, indicating that anticipatory strategies in human synchronization could naturally arise from processing rewards and timing errors. Our work contributes to the growing integration of machine learning techniques with cognitive neuroscience, offering new computational insights into the acquisition of timing skills. It establishes a flexible framework, which can be extended for future investigations in studying more complex rhythms, coordination between individuals, and even the neural basis of rhythm perception and production. / Thesis / Master of Science (MSc) / Have you ever wondered how we naturally tap our foot in time with music? This thesis investigates this human ability, known as sensorimotor synchronization, using artificial intelligence. By creating artificial agents that learn to tap along with a steady beat through reinforcement learning—like a person tapping to a metronome—we aimed to understand how the brain acquires this skill. Our experiments showed that how we define success, significantly affects how the agents learn the skill. Notably, when we rewarded both precise timing and consistent tapping, the agents' behavior closely resembled that of humans. They even exhibited a human-like pattern in error correction, making larger adjustments when tapping too late rather than too early. This research offers new insights into how our brains process and learn rhythm and timing. It also lays the groundwork for developing AI systems capable of replicating human-like timing behaviors, with potential applications in music technology and robotics.

The effects of non-differential reinforcement and differential reinforcement on problem behaviors and accuracy of responding of autistic children was examined. In experiment 1, one child with autism participated, and in experiment 2, two children with autism participated. In the non-differential reinforcement condition both prompted and unprompted responses were reinforced. In the differential reinforcement condition only unprompted responses were reinforced. Overall, problem behaviors were more frequent in the non-differential reinforcement condition. In experiment 1, accuracy was higher in the differential reinforcement condition, while experiment 2 showed inconclusive results with regards to accuracy. It is concluded that non-differential reinforcement can decrease problem behaviors in teaching situations, but may not be sufficient to ensure acquisition of target tasks.

Two groups of human subjects pressed buttons on five different variable-interval (VI) reinforcement schedules presented for seven minutes each for 15 sessions. At session 16, the same VI schedules were programmed concurrently in each session either with or without a 5 s changeover delay (COD). The same schedule-correlated stimuli were employed in single and concurrent conditions. Two other groups responded on concurrent VI VI conditions from the first session with or without the COD. Response allocations under concurrent scheduling better approximated relative reinforcement frequencies when the COD was programmed. Subjects with single VI histories failed to match response and time allocations to reinforcement ratios better than subjects given no such history. Bidirectional cumulative records are discussed as a molecular data analysis technique.

Negative reinforcement can be a powerful tool for behavior analysts, yet it is often overlooked as a treatment method. Pryor (1999) outlines a method for approaching a "timid" animal using a combination of negative reinforcement and positive reinforcement. When the animal stands still, the human operates a clicker, and then retreats from the animal. Gradually, the human moves closer to the animal through the clicking and retreating shaping process. Once the human is standing close enough, food may be offered as a positive reinforcer, and the negative reinforcer is canceled out. The purpose of this study was to experimentally demonstrate the click-retreat technique with cows. A multiple-baseline design across subjects was used to test this technique. Results show that the click and retreat technique was effective. Results are discussed in terms of the difference between the click-retreat technique and systematic desensitization.

Pigeons were trained on a zero-delay identity match-to-sample task. Experiment 1 started with every correct match reinforced with grain access and subsequent conditions include higher fixed-ratio values. Experiment 2 included the same fixed-ratio values as experiment 1 with and without a limited hold (LH) on the opportunity to select a comparison stimulus. Prior research suggested that trials after reinforcement would have an increased likelihood of error, and that these errors would be reduced in LH conditions. Results confirmed this expected error pattern and in most LH conditions errors were reduced early in the ratio.