In planning situations involving tight deadlines, a commonsense reasoner may spend a substantial amount of the available time in reasoning towards and about the formulation of the (partial) plan. This reasoning involves, but is not limited to (partial) plan formulation, asking decisions about available and conceivable alternatives, plan sequecing, and also plan failure and revision. However, the time taken in reasoning about a plan brings the deadline closer. The reasoner should therefore take account of the passage of time during that same reasoning, and this accounting must continously affect every decision under time-pressure. Step-logics were introduced as a mechanism for reasoning situated in time. We employ an extension of them here, called ‘active logics’, to create a logic-based planner that lets a time-situated reasoner keep track of a approaching deadline as he/she makes (and enacts) his/her plan, thereby treatilng all facets of planning (including plan-formation and its simultaneous of subsequent execution) as deadline-coupled. While an agent under severe time-pressure may spend a substantial amount of the available time in reasoning towards and about a plan of action, in a realistic setting the same agent must also measure up to two other crucial resource limitations as well, namely space and computation bounds. We address these concerns and offer some solution by introducing a limited short-term memory combined with a primitive relevance mechanism, and a limited-capacity inference engine. We propose heuristics to maximize an agent's chances of meeting a dedline within these additional realistic constraints. We give examples from commonsense planning, including ones we have solved and implemented in Prolog.

Formal real-time imagination is a term that may curiously describe the activities of a commonsense agent in a real-time setting in general, and in a tight deadline situation in particular. We briefly describe an ‘active-logic’ mechanism that fits this description. Temporal projection is an essential component of realtime planning. We draw a parallel between imagination as we understand it in human context and the capacity of the automated agent to formulate mental images of possible scenarios and plans of action in the course of its reasoning. We outline a treatment of temporal issues of significance to a time-situated reasoning mechanism in a dynamic setting with deadlines. The Yale shooting problem is a benchmark problem in temporal reasoning. We demonstrate how the active-logic planning mechanism successfully handles some interesting real-time variants of the Yale shooting problem. The solutions to each of these illustrate the agent’s ability to form contexts within which to reason, to project in each context thus formed by applying default inferences, and to revise and extend its conclusions within each context by applying time-sensitive inference rules, and most importantly, to account for all the time spent in the process.