Planning to perceive : A utilitarian approach

Perception is used to gather information that an agent needs in order to have the accurate internal representation of the external world that it requires so that it can make good planning decisions. Such information gathering is a planning task like any other, and perception is not the only method by which it can be carried out. Acts of perception should therefore be treated no differently from any other information-gathering acts. An opportunistic planner must be able to judge whether information is worth gathering in terms of its usefulness in deciding whether to take advantage of an opportunity and the cost of obtaining it. We propose that a well-characterized, widely applicable set of heuristics can offer a general method of making such judgments. 1 . Why gather information? Perception is a way of gathering information. In order to make reasonable decisions about what actions to perform, an agent must be able to incorporate information gained through perception into its representation of the world. This is true even of agents that do not reason explicitly, but merely react. For example, the agents described by Brooks (1986, 1990), have a simple representation of the world on the level of “There’s light in that direction. ” Agents that do reason explicitly about the world, in however rudimentary a manner, need more elaborate representations, so that they can answer such questions as “Is there a bee flying towards me? ” (Agre & Chapman 1987), “Is there a red rock here? ” and, possibly, “Where is there a red rock? ” (Firby 1989). Ideally, the agent’s internal representation at any given moment should reflect the actual state of the world at that moment. Insofar as it does not, the agent is in danger of making decisions based on false premises. Traditional planning systems ( e.g. Fikes & Nilsson 1971, Sacerdoti 1977, Chapman 1987) have relied heavily on projection to update their internal representations. While this method is adequate in a static, predictable world (such as classic representations of blocks world), it is problematic in a dynamic, unpredictable world. If the agent cannot have complete knowledge of the initial situation, because the world is too complex, or cannot predict all changes that may occur, because the agent’s own actions are not the only cause of changes, or cannot predict the effects of its own actions accurately, because they have uncertain outcomes, then projection cannot be relied upon and perception becomes far more critical. Perception, therefore, is needed in a dynamic, unpredictable world if the agent is to have an accurate internal representation of it. When Brooks (1990) says “the world is its own best model,” he is pointing out that the world is, by its very nature, a totally accurate representation of itself. This accuracy makes it tempting to forsake internal representation of the world almost entirely, using perception to gather information at the time when the agent needs it. Unfortunately for such an approach, many aspects of the world are not available for quick and easy inspection. To take an example from Hanks (1990), if there is a drawbridge on the route being considered by a robot delivery truck, the truck would like to know whether the bridge is up or down. Obviously, this information can in principle be gained by simply observing the bridge, the bridge being, as it were, a model of itself. However, if the truck is ten miles away, trying to decide whether or not to take the route that uses the drawbridge, this is not very helpful. It is because the cost of retrieving a needed piece of information from the world can vary so widely that an agent needs to have internal representations. The purpose of an internal model is to retain information that can no longer be acquired through perception. The difficulty is that an internal model must from time to time become incomplete, inaccurate, or out of date, because of the dynamism and unpredictability of the world. When this happens, the agent must try to rectify the situation by gathering the requisite information. For example, if an agent knows that its beliefs about a particular state of the world that it needs in order to make a decision are missing, inaccurate, or out of date, it will try to update them. Perception is one such way of realigning the model with the world, and another is to try to infer an accurate belief about the state from other information that is already in the model. The important issue for the agent is not whether to perform a particular act of perception, but whether or not it can obtain the accurate information it needs in order to make the decision. 1 . 1 How to gather information Agents gather information in response to a perceived need, starting from the prior need to make a decision. For example, if our robot truck must decide whether to take a certain route, and the decision depends on whether the drawbridge is up or down, the state of the drawbridge is a fact that the truck needs in order to make its decision. If it already knows the state of the drawbridge, and believes its knowledge to be accurate, then no further information is needed. It may be, however, that the truck has no specific belief about the state of the drawbridge, or is not confident that the belief it has is accurate. For example, it might believe that the bridge is down because an informant—John, say— just said that it was, but it might also believe that John is, in general, an untrustworthy informant. In either case, the truck has a goal to confirm or deny the proposition “the drawbridge is down. ” There are two ways in which to try to satisfy such a goal. The first is to try to make an inference from existing knowledge. Possible inferences in this instance include: • The bridge was down an hour ago when I passed it, so it is probably still down. • The bridge never goes up before noon, and it is ten now, so it will be down. • It’s -44 degrees, the river is frozen solid, no ships will be moving, so the bridge will be down. The second way to try to satisfy a goal to confirm or deny a proposition is to execute a perception plan, for example, to inspect the bridge visually. This particular perception plan requires that the truck be in sight of the bridge, and be able to resolve the bridge well enough visually to make the necessary distinction (up or down). The truck might carry out plans to achieve these preconditions if they are not met, for example, it might climb a hill to see the bridge, or use field glasses to obtain the necessary resolution. The two approaches, perception and inference, are intertwined. For example, the truck might not know the state of the bridge, but might realize that if it knew that the water were frozen, this would argue, by the inference mentioned above, that the bridge was down. Not knowing whether the water is frozen would in turn lead to a subgoal to confirm or deny the proposition, “The water is frozen. ” The truck might achieve this goal by carrying out a perception plan to observe the state of the water, or by another inference. Gathering information, then, is action in service of a goal, and should be treated by the planner in the same way as any other action in the service of any other goal. In particular, an agent should use the same procedures for deciding whether to perform an information-gathering action involving perception as it uses to decide whether to perform such an action involving inference. In other words, the basis of the decision should be the same as for any course of action that the agent considers: is performing the action an effective way of achieving the goal? More generally, will the benefits of performing it outweigh the costs? 1 . 2 Deciding to gather information In a sense, it would seem that acquiring information is always desirable. As we have seen, however, the gathering of information is an active endeavor, and has costs associated with it. Gathering information is a planning task (Pryor & Collins 1991). Since there will be alternative methods of gathering a given piece of information, an agent should choose among different methods in the same way as it should make any choice among actions. Perhaps the best model of how to make such decisions is to choose the option with the highest expected utility, in the standard sense used in economics (see Feldman & Sproull 1977). For an information-gathering action the benefit is the value of the information gathered, in the form of its usefulness in solving the problem at hand, and the cost is measured in the same way as for any other action. Viewing information gathering as a planning task means that among other things: • The information-gathering process begins with an explicit goal to acquire a piece of information. • Perception means executing one of a set of perception operators, which, if successfully executed, lead to the acquisition of a piece of information. • Inference similarly means applying one of a set of inference operators, which, if successfully executed, lead to the acquisition of a piece of information. There are several interesting implications of this view of information gathering: • Perception and inference may be alternative methods of establishing a given fact. The choice between them should be made on the basis of expected utility, taking into account the total costs and benefits of using each method. • Plans involving both perception and inference will be subplans of larger plans (such as getting from A to B), and their cost is part of the overall cost of those plans. Thus the choice of overall plan could depend on the ability to obtain information cheaply. • Information could cost more than it is worth, whatever the method used to obtain it. 2 . Using heuristics We are in the process of building a planner that is capable of opportunistically satisfying its goals; that can, for example recognize that a blue rock sitting by the side of the road presents an opportunity to achieve the goal of satisfying