Predicting Resource Use with Case- ecognit io

When managing a computing system, it is better to predict problems before they occur, rather than just observing them when they occur. Our research develops self-interested agents designed for the prediction of resource usages, that is, assessing the likelihood of upcoming demands by users on the limited resources and detecting potential problems from the observations of human-computer interactions (Etzioni & Weld 1994). Furthermore, these agents will have behavior that changes over time based on their own experiences, which will improve their predictive ability and allow them to adapt to changing usage patterns. There are two major sources of uncertainty : there is usually more than one plan consistent with the observations, and uncertainty about whether future actions in any given plan will be executed. The plan recognition problem in this work is to observe a user’s ongoing interactions with the computing environment, analyze those interactions by recognizing behaviors relevant to the agent’s interest, and use those relevant behaviors to make predictions about the user’s future behaviors. This is done incrementally: as the user performs actions, the system adjusts its set of possible plans (those consistent with the observations), and bases its predictions on the likelihood of each candidate plan and the likelihood of the behavior of interest within each plan. The predictions for individual users are then aggregated over all current users to predict system behavior. Some difficulties inherent in recognizing a user’s plans in this domain are the nonstrict temporal orderings of actions, the interleaving of multiple tasks, the large space of possible plans, and conditional plans where the condition is neither explicit nor directly observable. On the plus side, the language of discourse (UNIX commands and system responses) is relatively small, grammatically simple, and easy to monitor, given we are only concerned with textual interactions. Our approach to the plan recognition-prediction problems is based on a mixture of case-based reasoning and statistical prediction. Cases (past plan executions) are used to suggest plans consistent with observations, while statistical information (Markov model) is used to distinguish and evaluate alternatives and come up with