Abstract

In order for autonomous systems to interact with their environment in an intelligent way, they must be given the ability to adapt and learn incrementally and deliberately. It is virtually impossible to devise and hand code all potentially relevant domain knowledge for complex dynamic tasks. This thesis describes a framework to acquire domain knowledge for planning by failure-driven experimentation with the environment. The initial domain knowledge in the system is an approximate model for planning in the environment, defining the system's expectations. The framework exploits the characteristics of planning domains in order to search the space of plausible hypotheses without the need for additional background knowledge to build causal explanations for expectation failures. Plans are executed while the external environment is monitored, and differences between the internal state and external observations are …

Date

March 14, 1992

Authors

Yolanda Gil

Institution

Carnegie Mellon University