Andrew Carlson

AAAI Conference 2015 Conference Paper

Never-Ending Learning

• Tom Mitchell
• William Cohen
• Estevam Hruschka
• Partha Talukdar
• Justin Betteridge
• Andrew Carlson
• Bhavana Dalvi Mishra
• Matthew Gardner

Whereas people learn many different types of knowledge from diverse experiences over many years, most current machine learning systems acquire just a single function or data model from just a single data set. We propose a never-ending learning paradigm for machine learning, to better reflect the more ambitious and encompassing type of learning performed by humans. As a case study, we describe the Never- Ending Language Learner (NELL), which achieves some of the desired properties of a never-ending learner, and we discuss lessons learned. NELL has been learning to read the web 24 hours/day since January 2010, and so far has acquired a knowledge base with over 80 million confidence-weighted beliefs (e. g. , servedWith(tea, biscuits)), while learning continually to improve its reading competence over time. NELL has also learned to reason over its knowledge base to infer new beliefs from old ones, and is now beginning to extend its ontology by synthesizing new relational predicates. NELL can be tracked online at http: //rtw. ml. cmu. edu, and followed on Twitter at @CMUNELL.

IROS Conference 2012 Conference Paper

Aquapod: A small amphibious robot with sampling capabilities

• Sandeep Dhull
• Dario Canelon
• Apostolos D. Kottas
• Justin Dancs
• Andrew Carlson
• Nikolaos P. Papanikolopoulos

Mobile robots are often proposed as a favorable substitute to human correspondence in emergency response, disaster relief, and environmental monitoring scenarios. In this work, the next iteration of the Aquapod is proposed as a method to facilitate collection of subsurface liquid samples in order to assess toxicity levels in a body of water. This amphibious small form-factor robot is equipped with a buoyancy control unit, detachable fluidic sampling unit, and a wide range of sensing and processing capabilities. The robot was designed to move and collect water samples to a maximum depth of ten meters. Its unique form of tumbling locomotion results in a versatile platform that can be used in both terrestrial and aquatic environments leveraging its high mobility-to-size ratio.

ICRA Conference 2011 Conference Paper

Aquapod: Prototype design of an amphibious tumbling robot

• Andrew Carlson
• Nikolaos P. Papanikolopoulos

As mobile robots decrease in size so does their ability to traverse rough terrain. New forms of locomotion beyond the basic wheel are being explored to overcome this fault. This paper expands on the mechanical design of a previous robot with a high mobility-to-size ratio. To accomplish high mobility the robot uses tumbling as its form of locomotion. By actively involving the body of the robot in the locomotion it can scale larger obstacles and will not get stuck in compliant terrain like similar sized wheeled robots. To accommodate real-world environments the new design has been waterproofed and moreover can be completely submerged in water to operate on a lake or stream floor. Additionally, this robot is equipped with a buoyancy control unit which will allow the robot to either sink or float in water, offering many unique applications in environmental monitoring and surveillance. This paper describes a first generation, radio controlled prototype of the design.

AAAI Conference 2010 Conference Paper

Toward an Architecture for Never-Ending Language Learning

• Andrew Carlson
• Justin Betteridge
• Bryan Kisiel
• Burr Settles
• Estevam Hruschka
• Tom Mitchell

We consider here the problem of building a never-ending language learner; that is, an intelligent computer agent that runs forever and that each day must (1) extract, or read, information from the web to populate a growing structured knowledge base, and (2) learn to perform this task better than on the previous day. In particular, we propose an approach and a set of design principles for such an agent, describe a partial implementation of such a system that has already learned to extract a knowledge base containing over 242, 000 beliefs with an estimated precision of 74% after running for 67 days, and discuss lessons learned from this preliminary attempt to build a never-ending learning agent.

ICML Conference 2008 Conference Paper

On the chance accuracies of large collections of classifiers

• Mark Palatucci
• Andrew Carlson