People often do not know where things are and have to look for them. This thesis presents a formal model suitable for reasoning about how to find things and acting to find them, which I will call “search behavior”. Since not knowing location of something can prevent an agent from reaching his desired goal, the ability to plan and conduct a search will be argued to increase the variety of situations in which an agent can succeed at his chosen task. Searching for things is a natural problem that arises when the blocks world assumptions (which have been the problem setting for most planning research) are modified by providing the agent only partial knowledge of his environment. Since the agent does not know the total world state, actions may appear to have nondeterministic effects. The significant aspects of the search problem which differ from previously studied planning problems are the acquisition of information and iteration of similar actions while exploring a search space. Since introduction of the situation calculus [MH69], various systems have been proposed for representing and reasoning about actions which involve knowledge acquisition and iteration, including Moore's work on the interaction between knowledge and action [Moo80]. Such systems can be used to infer properties of plans which have already been constructed, but do not themselves construct plans for complex actions. My concern with searching has to do with a sense that Moore's knowledge preconditions are overly restrictive. Morgenstern [Mor88] examined ways to weaken knowledge preconditions for an individual agent by relying on the knowledge and abilities of other agents. Lesperance's research [Les91] on indexical knowledge is another way of weakening the knowledge preconditions. I am trying to reduce the amount of information an agent must know (provided he can search a known search space). If you dial the right combination to a safe it will open, whether or not you knew in advance that it was the right combination. Search is a way to guarantee you will eventually dial the right combination. So what I am exploring is how to systematically construct a search that will use available knowledge to accomplish something the agent does not currently know enough to do directly. I claim it is possible for automated agents to engage in search behavior. Engaging in search behavior consists in recognizing the need for a search, constructing an effective plan, and then carrying out that plan. Expressing such a plan and reasoning about its effectiveness requires a representation language. I will select a representation language based on criteria derived from analyzing the search planning problem. Each of the three components of a system for engaging in search behavior will be designed and implemented to demonstrate that an automated agent can find things when he needs to.