Teaching an Old Robot New Tricks: Learning Novel Tasks via Interaction with People and Things

As AI has begun to reach out beyond its symbolic, objectivist roots into the embodied, experientialist realm, many projects are exploring different aspects of creating machines which interact with and respond to the world as humans do. Techniques for visual processing, object recognition, emotional response, gesture production and recognition, etc., are necessary components of a complete humanoid robot. However, most projects invariably concentrate on developing a few of these individual components, neglecting the issue of how all of these pieces would eventually fit together. The focus of the work in this dissertation is on creating a framework into which such specific competencies can be embedded, in a way that they can interact with each other and build layers of new functionality. To be of any practical value, such a framework must satisfy the real-world constraints of functioning in real-time with noisy sensors and actuators. The humanoid robot Cog provides an unapologetically adequate platform from which to take on such a challenge. This work makes three contributions to embodied AI. First, it offers a general-purpose architecture for developing behavior-based systems distributed over networks of PC's. Second, it provides a motor-control system that simulates several biological features which impact the development of motor behavior. Third, it develops a framework for a system which enables a robot to learn new behaviors via interacting with itself and the outside world. A few basic functional modules are built into this framework, enough to demonstrate the robot learning some very simple behaviors taught by a human trainer. A primary motivation for this project is the notion that it is practically impossible to build an "intelligent" machine unless it is designed partly to build itself. This work is a proof-of-concept of such an approach to integrating multiple perceptual and motor systems into a complete learning agent.

Experimental Markets for Product Concepts

Market prices are well known to efficiently collect and aggregate diverse information regarding the value of commodities and assets. The role of markets has been particularly suitable to pricing financial securities. This article provides an alternative application of the pricing mechanism to marketing research - using pseudo-securities markets to measure preferences over new product concepts. Surveys, focus groups, concept tests and conjoint studies are methods traditionally used to measure individual and aggregate preferences. Unfortunately, these methods can be biased, costly and time-consuming to conduct. The present research is motivated by the desire to efficiently measure preferences and more accurately predict new product success, based on the efficiency and incentive-compatibility of security trading markets. The article describes a novel market research method, pro-vides insight into why the method should work, and compares the results of several trading experiments against other methodologies such as concept testing and conjoint analysis.

Exact Solution of the Nonlinear Dynamics of Recurrent Neural Mechanisms for Direction Selectivity

Different theoretical models have tried to investigate the feasibility of recurrent neural mechanisms for achieving direction selectivity in the visual cortex. The mathematical analysis of such models has been restricted so far to the case of purely linear networks. We present an exact analytical solution of the nonlinear dynamics of a class of direction selective recurrent neural models with threshold nonlinearity. Our mathematical analysis shows that such networks have form-stable stimulus-locked traveling pulse solutions that are appropriate for modeling the responses of direction selective cortical neurons. Our analysis shows also that the stability of such solutions can break down giving raise to a different class of solutions ("lurching activity waves") that are characterized by a specific spatio-temporal periodicity. These solutions cannot arise in models for direction selectivity with purely linear spatio-temporal filtering.

Kinetics and dynamics measured using IntraCavity Laser Absorption Spectroscopy

IntraCavity Laser Absorption Spectroscopy (ICLAS) is a high-resolution, high sensitivity spectroscopic method capable of measuring line positions, linewidths, lineshapes, and absolute line intensities with a sensitivity that far exceeds that of a traditional multiple pass absorption cell or Fourier Transform spectrometer. From the fundamental knowledge obtained through these measurements, information about the underlying spectroscopy, dynamics, and kinetics of the species interrogated can be derived. The construction of an ICLA Spectrometer will be detailed, and the measurements utilizing ICLAS will be discussed, as well as the theory of operation and modifications of the experimental apparatus. Results include: i) Line intensities and collision-broadening coefficients of the A band of oxygen and previously unobserved, high J, rotational transitions of the A band, hot-band transitions, and transitions of isotopically substituted species. ii) High-resolution (0.013 cm-1) spectra of the second overtone of the OH stretch of trans-nitrous acid recorded between 10,230 and 10,350 cm-1. The spectra were analyzed to yield a complete set of rotational parameters and an absolute band intensity, and two groups of anharmonic perturbations were observed and analyzed. These findings are discussed in the context of the contribution of overtone-mediated processes to OH radical production in the lower atmosphere.