Self-organizing event maps

To take further steps along the path toward true artificial intelligence, systems must be built that are capable of learning about the world around them through observation and explanation. These systems should be flexible and robust in the style of the human brain and little precompiled knowledge should be given initially. As a step toward achieving this lofty goal, this thesis presents the self-organizing event map (SOEM) architcture. The SOEM architecture seeks to provide a way in which computers can be taught, through simple observation of the world, about typical events in a way that organized according to events that are observed by the system. In this manner, the event map produces clusters of similar events and provides an implicit representation of the regularity within the event space to which the system has been exposed. As part of this thesis, a test system that makes use of self-organizing event map architecture has been developed in conjunction with the Genesis Project at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. This system receives input through a natural-language text interface and, through repreated training cycles, becomes capable of discerning between typical and exceptional events. Clusters of similar events develop within the map and these clusters act as an implicit is flexible and robust. The self-organizing event map, as a data structure, stores a plane of event models that are continually updated and form of the more commonly used (and explicit) notion of scripts and capability lists. For example, a trained map may recognize that dogs often run, but never fly. Therefore if a new input is received that describes a flying dog, the map would be capable of identifying the event as exceptional

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2004

Author dc:creator

• Tardiff, Seth Ronald, 1981-

Advisor dc:contributor.advisor

• Patrick Henry Winston.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/17982

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/17982