It assumes that the Consider the problem faced by a unmanned aerial vehicle probability distribution over incoming goals is either known (UAV) dispatcher who must plan for a set of UAVs to service or learn-able and employs the technique of optimization a set of observation requests. To service a request, one of the in hindsight, previously developed for online scheduling UAVs must fly over a given strip of land with its observation and recently investigated for planning with stochastic actions equipment turned on. The dispatcher wants to minimize the (Mercier and van Hentenryck 2007; Yoon et al. 2008; time between when a request arrives and when an UAV has 2010). This technique first samples from the distribution of completed the flyover. Even when the actions of the UAV, possible future goal arrivals and then considers which next such as flying particular routes or switching on/off observational action optimizes the expected cost when averaged over the equipment, can be regarded as deterministic, the sampled futures. By using this anticipatory technique, our stochastic arrival of new requests can make for a challenging planner is able to take future goals into account.

My research represents an approach to integrating planning and execution in time-sensitive environments. The primary focus is on a problem called continual on-line planning. New goals arrive stochastically during execution, the agent issues actions for execution one at a time, and the environment is otherwise deterministic. My dissertation will address this setting in three stages: optimizing total goal achievement time, handling on-line goal arrival during planning or execution, and adapting to changes in state also during planning or execution. My current approach to this problem is based on incremental heuristic search. The two central issues are the decision of which partial plans to elaborate during search and the decision of when to issue an action for execution. I have proposed an extension of Russell and Wefald's decision-theoretic A* algorithm that is not limited by assumptions of an admissible heuristic like DTA*. This algorithm, Decision Theoretic On-line Continual Search (DTOCS), handles the complexities of the on-line setting by balancing deliberative planning and real-time response.