Abstract
In real-world applications, the eective integration of learn-
ing and reasoning in a cognitive agent model is a dicult
task. However, such integration may lead to a better under-
standing, use and construction of more realistic multiagent
models. Existing models are either oversimplied or require
too much processing time, which is unsuitable for online
learning and reasoning. In particular, higher-order concepts
and cognitive abilities have many unknown temporal rela-
tions with the data, making it impossible to represent such
relationships by hand. In this paper, we develop and apply a
Neural-Symbolic Cognitive Agent (NSCA) model for online
learning and reasoning that seeks to eectively represent,
learn and reason in complex real-world applications.
ing and reasoning in a cognitive agent model is a dicult
task. However, such integration may lead to a better under-
standing, use and construction of more realistic multiagent
models. Existing models are either oversimplied or require
too much processing time, which is unsuitable for online
learning and reasoning. In particular, higher-order concepts
and cognitive abilities have many unknown temporal rela-
tions with the data, making it impossible to represent such
relationships by hand. In this paper, we develop and apply a
Neural-Symbolic Cognitive Agent (NSCA) model for online
learning and reasoning that seeks to eectively represent,
learn and reason in complex real-world applications.
| Original language | English |
|---|---|
| Title of host publication | International conference on Autonomous Agents and Multi-Agent Systems, AAMAS '14, Paris, France, May 5-9, 2014 |
| Pages | 1621-1622 |
| Number of pages | 2 |
| Publication status | Published - 2014 |