A computational theory of motor learning

In this paper we present a computational theory of human motor performance and learning. The theory is implemented as a running AI system called MAGGIE. Given a description of a desired movement as input, the system generates simulated motor behavior as output. The theory states that skills are encoded as motor schemas, which specify the positions and velocities of a limb at selected points in time. Moreover, there exist two natural representations for such knowledge: viewer-centered schemas describe visually perceived behavior, and joint-centered schemas are used to generate behavior. When the model acts upon these two representational formats, they exhibit quite different behavioral characteristics. MAGGIE performs the desired movement within a feedback control paradigm, monitoring for errors and correcting them when it detects them. Learning involves improving the joint-centered schema over many practice trials; this reduces the need for monitoring. The model accounts for a number of well-documented motor phenomena, including the speed-accuracy trade-off and the gradual improvement in performance with practice. It also makes several testable predictions. We close with a discussion of the theory's strengths and weaknesses, along with directions for future research

Acquisition and improvement of human motor skills: Learning through observation and practice

Skilled movement is an integral part of the human existence. A better understanding of motor skills and their development is a prerequisite to the construction of truly flexible intelligent agents. We present MAEANDER, a computational model of human motor behavior, that uniformly addresses both the acquisition of skills through observation and the improvement of skills through practice. MAEANDER consists of a sensory-effector interface, a memory of movements, and a set of performance and learning mechanisms that let it recognize and generate motor skills. The system initially acquires such skills by observing movements performed by another agent and constructing a concept hierarchy. Given a stored motor skill in memory, MAEANDER will cause an effector to behave appropriately. All learning involves changing the hierarchical memory of skill concepts to more closely correspond to either observed experience or to desired behaviors. We evaluated MAEANDER empirically with respect to how well it acquires and improves both artificial movement types and handwritten script letters from the alphabet. We also evaluate MAEANDER as a psychological model by comparing its behavior to robust phenomena in humans and by considering the richness of the predictions it makes

Real Simulations and Simulated Reality

Movies such as The Matrix have stimulated popular interest in “brain in a vat” scenarios. Amidst the traditional questions of the mind, we tend to overlook an integral enabling component—the world simulation—which merits consideration in its own right. When facing the simulations in these imagined scenarios, we struggle with conceptual muddles regarding what is real and not. In this paper, I argue that simulated worlds are every bit as real as the one we inhabit. This turns out to be important when considering the possibility, as suggested by Nick Bostrom, that the world we experience as “real” is actually a simulation. Can such a prospect be reconciled with an Orthodox Christian perspective? While the metaphysical status of simulations that I posit moves us towards an integration, significant obstacles remain to be addressed. I consider some of these remaining challenges and explore the associated stakes

Human motor behavior : a short review of phenomena, theories, and systems

In this paper we survey three facets of human motor behavior - phenomena, theories, and implementations. We are particularly concerned with motor behavior that exhibits improvements over time and practice; this is referred to as human motor learning. We begin by discussing both performance and learning phenomena that have been observed repeatedly in laboratory situations. This is followed with a review of three prominent theories of human motor control from the psychological literature. These phenomena serve as a foundation on which to compare these theories. Finally, we consider several implemented models of motor learning that have attended to constraints imposed either by the phenomena introduced earlier, or by the physiological structure of the human neuro-muscular system. From the material surveyed, we conclude that more research on computational models would help answer remaining open questions in human motor behavior

A Computational Account of Movement Learning and its Impact on the Speed-Accuracy Tradeoff

We present a computational model of movement skill learning. The types of skills addressed are a class of trajectory following movements involving multiple accelerations, decelerations and changes in direction and lasting more than a few seconds. These skills are acquired through observation and improved through practice. We also review the speed-accuracy tradeoff---one of the most robust phenomena in human motor behavior. We present two speed-accuracy tradeoff experiments where the model's performance fits human behavior quite well. Introduction Highly skilled movement is impressive to behold and a challenge to exhibit, whether it be championship figure skating or an intricate surgical procedure. The acquisition, development, and maintenance of such levels of skill are equally intriguing. Theories tend to grow out of the phenomena that are identified through studies on human (and animal) movement. Computational models of skilled movement can help us test and refine these theories. Ou..

When Is Assistance Really Helpful

We regularly operate under the notion that one agent assists another when the first does something for the second. However, the story behind this is much more complicated. In this position paper, we explore two questions: How can we evaluate the quality or goodness of a particular instance of assistance? and How can we design agents to initiate and provide “good ” assistance? In asking these questions, we are assuming two things. First, performing a task for another agent is not always helpful to that agent even if the task really needs to be done. But second, a helper that does not have the skill or resources to accomplish a specifically requested task may still provide real help. Our experimental work attempts to address the fundamental elements of helpful assistance. One surprising initial result reminds us o

Adapting to User Preferences in Crisis Response

The domain of crisis planning and scheduling taxes human response managers due to high levels of urgency and uncertainty. Such applications require assistant technologies (in contrast to automation technologies) and provide special challenges for interface design. We present INCA, the INteractive Crisis Assistant, that helps users develop effective crisis response plans and schedules in a timely manner. INCA also adapts to the individual users by anticipating their preferred responses to a given crisis and their intended repairs to a candidate response. We evaluate our system in HAZMAT, a synthetic hazardous materials incident domain. The results show that INCA tailors itself to individual users and provides effective support for the timely generation of effective responses