Inducing Design Biases that Characterize Successful Experimentation in Weak-Theory Domains: TIPS

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 13

AAAI-96 Student Abstracts

Experiment design in domains with weak theories is largely a trial-and-error process. In such domains, the effects of actions are unpredictable due to insufficient knowledge about the causal relationships among entities involved in an experiment. Thus, experiments are designed based on heuristics obtained from prior experience. Assuming that past experiment designs leading to success or failure can be recorded electronically, this thesis research proposes one method for analyzing these designs to yield hints regarding effective operator application sequences. This work assumes that the order in which operators are applied matters to the overall success of experiments. Experiment design can also be thought of as a form of planning, since it involves generation of a sequence of steps comprising of one or more operations that can change the environment by changing values of some of the parameters that describe the environment. Experiment design operators can therefore be thought of as plan operators at higher levels of abstraction. This thesis proposes a method for learning contexts within which applying certain sequences of operators has favored successful experimentation in the past.