Often an agent that has to solve a problem must choose which heuristic or strategy will help it the most in achieving its objectives. Sometimes the agent wishes to obtain additional units of information on the possible heuristics and strategies in order to choose between them, but it may be costly. As a result, the agent's goal is to acquire enough units of information in order to make a decision while incurring minimal cost. We focus on situations where the agent must decide in advance how many units it would like to obtain. We present an algorithm for choosing between two options, and then formulate three methods for the general case where there are k > 2 options to choose from. We investigate the 2-option algorithm and the general k-option methods effectiveness in two domains: the 3-SAT domain, and the CT computer game. In both domains we present the experimental performance of our models. Results will show that applying the 2-option algorithm is beneficial and provides the agent a substantial gain. In addition, applying the k-option method in the domains investigated results in a moderate gain.
|Original language||American English|
|Title of host publication||International Joint Conference on Artificial Intelligence|
|Publisher||LAWRENCE ERLBAUM ASSOCIATES LTD|
|State||Published - 2005|