For modeling knowledge with time constraints we need to use some model of time measurement. As remarked previously, we shall deal with sentences of the form ``if asked about , agent can derive reliably the answer within time units''. For simplicity we shall use natural numbers to measure time, i.e., we assume that is a natural number. So the language we consider should contain formulae of the form where is the name of an agent, is a natural number, and is a formula. The formula can be read ``agent knows within units of time'' and is interpreted: ``if agent chooses to derive from his current knowledge, then after at most time units he will succeed'', or alternatively, ``if asked about , is able to derive reliably the answer 'yes' within units of time''. That is, we require not only that agent have at least one procedure to compute , but also that be able to choose the correct procedure leading to under the given time constraint, namely, to arrive at the conclusion after at most time units5.2.
Sometimes it can be assumed safely that an agent is able to infer some fact, but it is not possible to estimate accurately how long the computation would take. For example, the complexity of the employed algorithm or the agent's inference strategy may not be known completely. To deal with such cases we introduce a sort of quantification into the language. We consider statements of the form ``there is a number such that the agent is able to compute the fact within units of time''. Such a statement is formalized by the formula , which can be read: ``agent can infer reliably in finite time''. That is, when presented with the fact , the agent is able to choose a suitable algorithm which runs on and terminates with the (correct) answer after finitely many steps.
The formula entails the following facts about the agent's information state. First, the formula follows (with respect to the logic used by ) from all what knows. Second, agent has an algorithm to establish that connection and which he is able to select to use when he chooses to compute . Third, that computation takes at most time unit. The formula is weaker in the sense that it does not tell exactly how long the computation of will take. It only says that the computation is guaranteed to terminate.
Our notion of knowledge can be called algorithmic knowledge: knowledge is tied up with an algorithm to establish it. It represents not only factual knowledge but also a kind of procedural knowledge. The concepts of explicit and implicit knowledge can be regarded naturally as two special cases of algorithmic knowledge. Explicit knowledge can be defined as , which says that agent has immediate access to the information and can act on it. Implicit knowledge is defined as : agent knows implicitly if he is able to compute when required. This is, however, not the only way to define a useful notion of implicit knowledge. For instance, one can stipulate that an agent knows a fact implicitly it can be inferred from his explicit knowledge (with respect to some inference system).
Our use of the term ``algorithmic knowledge'' as explained above should not be confused with other usages found elsewhere in the literature. Binmore and Shin ([BS92]) use the term to emphasize the connection between knowledge and provability (see also [SW94]). In their terminology, an agent's algorithmic knowledge is whatever the agent can infer using a Turing machine. The properties of this concept are studied and related to properties of provability concepts. Halpern, Moses, and Vardi ([HMV94]) also define algorithmic knowledge in terms of computation: an agent is said to know a fact at a certain state if at that state he can compute that he knows that fact. That is, given his local data, his local algorithm terminates and outputs the answer ``Yes'' when presented with the fact. Clearly, these concepts describe knowledge that is not necessarily available immediately to the agent. They are in spirit related to our concept of implicit knowledge, defined above as . Hence, they both fall under the category of implicit knowledge in our classification of chapter 2: they characterize a kind of information that is implicitly available to an agent but must be computed and made explicit before the agent can act upon.
Formally, the language of algorithmic knowledge for agents is defined as follows:
The rationality of agents is expressed through two capacities: first, the ability to draw logical consequences from what is already known, and second, the ability to compute the complexities of certain reasoning problems in order to infer when something can be known. It should be stressed that these two capacities are implementable. Agents have been frequently supplied with inference machines which allow them to infer new information from what has been known. The complexities of many problems can be computed at a low cost from their syntactic structures alone, so it is not hard to build into agents the capability to recognize the structure of a problem and estimate the cost to solve it. It turns out that we can develop quite rich theories of the algorithmic notion of knowledge we have introduced. To develop logics of algorithmic knowledge we try to establish logical relationships among the formulae of the language . This is done by developing the framework for reasoning about explicit knowledge (chapter 4) a step further.