Acquiring Domain Knowledge for Planning by Experimentation
CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE
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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 systems 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 detected by various methods each correlated with a typical cause for the expectation failure. The methods also construct a set of concrete hypotheses to repair the knowledge deficit. After being heuristically filtered, each hypothesis is tested in turn with an experiment. After the experiment is designed, a plan is constructed to achieve the situation required to carry out the experiment. The experiment plan must meet constraints such as minimizing plan length and negative interference with the main goals.