Spaceborne Photonics Institute

This report describes in chronological detail the development of the Spaceborne Photonics Institute as a sustained research effort at Hampton University in the area of optical physics. This provided the research expertise to initiate a PhD program in Physics. Research was carried out in the areas of: (1) modelling of spaceborne solid state laser systems; (2) amplified spontaneous emission in solar pumped iodine lasers; (3) closely simulated AM0 CW solar pumped iodine laser and repeatedly short pulsed iodine laser oscillator; (4) a materials spectroscopy and growth program; and (5) laser induced fluorescence and atomic and molecular spectroscopy

Using informative behavior to increase engagement while learning from human reward

In this work, we address a relatively unexplored aspect of designing agents that learn from human reward. We investigate how an agent’s non-task behavior can affect a human trainer’s training and agent learning. We use the TAMER framework, which facilitates the training of agents by human-generated reward signals, i.e., judgements of the quality of the agent’s actions, as the foundation for our investigation. Then, starting from the premise that the interaction between the agent and the trainer should be bi-directional, we propose two new training interfaces to increase a human trainer’s active involvement in the training process and thereby improve the agent’s task performance. One provides information on the agent’s uncertainty which is a metric calculated as data coverage, the other on its performance. Our results from a 51-subject user study show that these interfaces can induce the trainers to train longer and give more feedback. The agent’s performance, however, increases only in response to the addition of performance-oriented information, not by sharing uncertainty levels. These results suggest that the organizational maxim about human behavior, “you get what you measure”—i.e., sharing metrics with people causes them to focus on optimizing those metrics while de-emphasizing other objectives—also applies to the training of agents. Using principle component analysis, we show how trainers in the two conditions train agents differently. In addition, by simulating the influence of the agent’s uncertainty–informative behavior on a human’s training behavior, we show that trainers could be distracted by the agent sharing its uncertainty levels about its actions, giving poor feedback for the sake of reducing the agent’s uncertainty without improving the agent’s performance

Facial identity across the lifespan

We can recognise people that we know across their lifespan. We see family members age, and we can recognise celebrities across long careers. How is this possible, despite the very large facial changes that occur as people get older? Here we analyse the statistical properties of faces as they age, sampling photos of the same people from their 20s to their 70s. Across a number of simulations, we observe that individuals’ faces retain some idiosyncratic physical properties across the adult lifespan that can be used to support moderate levels of age-independent recognition. However, we found that models based exclusively on image-similarity only achieved limited success in recognising faces across age. In contrast, more robust recognition was achieved with the introduction of a minimal top-down familiarisation procedure. Such models can incorporate the within-person variability associated with a particular individual to show a surprisingly high level of generalisation, even across the lifespan. The analysis of this variability reveals a powerful statistical tool for understanding recognition, and demonstrates how visual representations may support operations typically thought to require conceptual properties

Incorporating Advice Into Agents That Learn From Reinforcements

Learning from reinforcements is a promising approach for creating intelligent agents. However, reinforcement learning usually requires a large number of training episodes. We present an approach that addresses this shortcoming by allowing a connectionist Q-learner to accept advice given, at any time and in a natural manner, by an external observer. In our approach, the advice-giver watches the learner and occasionally makes suggestions, expressed as instructions in a simple programming language. Based on techniques from knowledge-based neural networks, these programs are inserted directly into the agent's utility function. Subsequent reinforcement learning further integrates and refines the advice. We present empirical evidence that shows our approach leads to statistically-significant gains in expected reward. Importantly, the advice improves the expected reward regardless of the stage of training at which it is given. Introduction A successful and increasingly popular method for cr..

Using Explanation-Based Learning to Acquire Programs By Analyzing Examples

A number of problems confront standard automatic programming methods. One problem is that the combinatorics of search make automatic programming intractable for most real-world applications. Another problem is that most automatic programming systems require the user to express information in a form that is too complex. Also, most automatic programming systems do not include mechanisms for incorporating and using domain-specific knowledge. One approach that offers the possibility of dealing with these problems is the application of explanation-based learning (EBL). In the form of EBL used for this project, explanation-based learning by observation, the user enters a description of a specific problem and solution to that problem in a form comfortable to him or her. Using domain-specific knowledge, the system constructs an explanation of the solution to the problem using the actions of the user as guidance. Next, the goal stated by the user is generalized with respect to any domain information about possible goals of actions performed by the user in the solution. Then the explanation is reconstructed with respect to the generalized goal. Finally, the explanation is transformed into a general solution which can be used to solve problems that are conceptually similar to the specific problem presented. This approach promises to overcome the problems with standard automatic programming methods discussed above