Intellego

Ontology of Perception: IntellegO (Greek: "to perceive")

Today, many sensor networks and their applications employ a brute force approach to collecting and analyzing sensor data. Such an approach often wastes valuable energy and computational resources by unnecessarily tasking sensors and generating observations of minimal use. People, on the other hand, have evolved sophisticated mechanisms to efficiently perceive their environment. One such mechanism includes the use of background knowledge to determine what aspects of the environment to focus our attention. In this project, we develop an ontology of perception, IntellegO, that may be used to more efficiently convert observations into perceptions. IntellegO is derived from cognitive theory, encoded in set-theory, and provides a formal semantics of machine perception.

System Architecture

Perception Cycle

Figure 1. Perception Cycle.

Perception Process

Figure 2. Perception Process.

Observation Process

Figure 3. Observation Process.

Ontologies and Knowledge Bases

Specification of Perception Cycle in Set Theory

Figure 4. Specification of Perception Cycle in Set Theory.

Weather Background Knowledge

Figure 5. Weather Background Knowledge

• Weather Background Knowledge
• Observation Knowledge Base
• Perception Ontology
• Perception Datasets:
  • 25 miles (17 observers)
  • 50 miles (70 observers)
  • 100 miles (170 observers)
  • 200 miles (373 observers)
  • 400 miles (516 observers)

Demonstrations

• Demo visualization of the Perception Cycle with Weather Background Knowledge

Statistics

Percepts Generated during Evaluation: # and %

(p = precipitation, t = temperature, w = wind speed)

25 miles (17 observers)

Figure 6.

50 miles (70 observers)

Figure 7.

100 miles (170 observers)

Figure 8.

200 miles (373 observers)

Figure 9.

400 miles (516 observers>

Figure 10.

Percepts Generated during Evaluation: Trends

Grounded Percepts

Figure 11.

Extraneous Percepts

Figure 12.

Theories Generated during Evaluation

Figure 13.