Adaptive Mixed-Initiative Systems for Decision-Theoretic Crisis Response

A crisis is characterized by three elements: threat, urgency, and uncertainty (Rosenthal et al. 1989). In crisis, an agent risks losing something of great value. The agent must take action quickly because without intervention, this undesirable situation will be realized in a limited amount of time. Different actions will generally incur different costs and have different outcomes under different circumstances. The agent’s decision-making process is further complicated by various sources of uncertainty--noisy information, exogenous events, actions with nondeterministic outcomes, etc. Information-gathering actions may reduce uncertainty, but they incur additional costs, not the least of which is the time so ill-afforded in crisis domains. Consider an incident involving the spill of some hazardous material in a factory. Unabated, the spill may damage the floors of the building, release toxic fumes that threaten the health of the factory workers, and seep into waterways to pollute the environment. Rapid response is necessary to contain the spill and minimize its harmful effects. Because of limited resources, the response team will not be able to completely avoid all the negative consequences. Their task is complicated by uncertainty--incomplete information about the involved material, unpredictable weather, inaccurate information about the building and its occupants, and so on. Gathering more information may reduce this uncertainty, but in the time this takes, the magnitude and the scope of the problem presented by the spill will continue to increase. Decision theory seems a natural approach, given the characteristics of crisis. However, decision-theoretic techniques are particularly difficult to implement for crisis domains for the following reasons. Decisiontheoretic methods require the development of complete models, a time-consuming task ill-afforded in crisis domains with their characteristic of urgency. The limited amount of time for decision-making also constrains the development of alternative courses of action and their subsequent consideration. Furthermore, the uncertainty inherent in crisis domains makes the enumeration of alternatives as well as the elicitation of probability models more difficult. However, decision-theoretic methods remain attractive for crisis domains because they provide wellfounded principles for making critical decisions. They force an agent to explicitly enumerate the values of imPortance, the alternative courses of action, and the unCertainty of the available information. This allows rational decision-making with clear justifications in highstress and often volatile situations. The key for crisis response is to be able to rapidly develop adequate decision models and use them efficiently. Towards this objective, we propose the use of machine learning techniques to learn user preferences 1