Combining deduction and abduction : toward an integrated theory of information processing.

Abstract Not Provide

An Application of Transfer to American Football: From Observation of Raw Video to Control in a Simulated Environment

Automatic transfer of learned knowledge from one task or domain to another offers great potential to simplify and expedite the construction and deployment of intelligent systems. In practice however, there are many barriers to achieving this goal. In this article, we present a prototype system for the real-world context of transferring knowledge of American football from video observation to control in a game simulator. We trace an example play from the raw video through execution and adaptation in the simulator, highlighting the system’s component algorithms along with issues of complexity, generality, and scale. We then conclude with a discussion of the implications of this work for other applications, along with several possible improvements

Scalable knowledge acquisition through cumulative learning and memory organization

The field of machine learning is dedicated to the process of finding and acquiring new knowledge automatically. A majority of research in the field is based on the assumption that knowledge of a domain may always be learned and stored using a flat, unstructured representation. In this work we assert the importance of learning in a structured environment, and of using a structured representation. We take the view that intermediate knowledge representations are of equal importance to high-level target knowledge. Development of intermediate representations is critical to subsequent learning, and requires at least as much learning effort as the high-level learning goals. Most importantly, we assert that intermediate knowledge must be organized in order for an agent to achieve its full learning potential. The goals of this research are to investigate the benefits of learning in a structured environment, and to demonstrate the mechanisms by which an agent can accumulate and organize knowledge. We focus on understanding how structured learning can produce highly compact representations, and how intermediate learning problems can remain tractable regardless of the size or complexity of the high-level target learning problem. Toward this end, we propose and evaluate a cumulative learning algorithm, SCALE, which acquires and organizes knowledge from a structured learning environment. We then compare the performance of SCALE with several algorithms from the machine learning literature, and demonstrate the importance and effects of memory organization on the learning process and scalability. We then conclude by highlighting several lessons learned regarding the nature of the structure learning problem, and key areas for future exploration

Memory Organization and Knowledge Transfer

An important aspect of learning is the ability to transfer knowledge from one domain to another. Recent transfer research has focused on the basic problem of how knowledge structures may be transferred and reused. In this paper, we consider the larger problem of how a learner can select the appropriate knowledge structures to transfer when many are available. We propose that previously acquired knowledge must be organized, and demonstrate one possible approach. 1

Scalable knowledge acquisition through memory organization

Memory organization plays a critical role in knowledge acquisition. An agent must select a small subset of existing knowledge to serve as the basis for new learning; otherwise each problem becomes more complex than the previous. Selecting this subset remains a challenge, however. We propose that existing knowledge be organized in order for a learning agent to achieve its full potential. The SCALE algorithm is presented as a method for knowledge acquisition and organization, and is used to demonstrate both the computational and training benefits of memory organization. 1

Many-Layered Learning

We explore incremental assimilation of new knowledge by sequential learning. Of particular interest is how a network of many knowledge layers can be constructed in an on-line manner, such that the learned units represent building blocks of knowledge that serve to compress the overall representation and facilitate transfer. We motivate the need for many layers of knowledge, and we advocate sequential learning as an avenue for promoting construction of layered knowledge structures. Finally, our novel STL algorithm demonstrates an efficient method for simultaneously acquiring and organizing a collection of concepts and functions from a stream of rich but otherwise unstructured information.

Many-Layered Learning 1

Abstract: We explore incremental assimilation of new knowledge by sequential learning. Of particular interest is how a network of many knowledge layers can be constructed in an on-line manner, such that the learned units represent building blocks of knowledge that serve to compress the overall representation and facilitate transfer. We motivate the need for many layers of knowledge, and we advocate sequential learning as an avenue for promoting construction of layered knowledge structures. Finally, our novel STL algorithm demonstrates a method for simultaneously acquiring and organizing a collection of concepts and functions as a network from a stream of unstructured information.