Thinking takes time: a modal active-logic for reasoning in time

Most common sense reasoning formalisms do not account for the passage of time a s the reasoning occurs, and hence are inadequate from the point of view of modeling an agent's {\em ongoing} process of reasoning. We present a modal active-logic that treats time as a valuable resource that is consumed in each step of the agent's reasoning. We provide a sound and complete characterization for this logic and examine how it addresses the problem of logical omniscience. (Also cross-referenced as UMIACS-TR-94-39

Active Logics: A Unified Formal Approach to Episodic Reasoning

Artificial intelligence research falls roughly into two categories: formal and implementational. This division is not completely firm: there are implementational studies based on (formal or informal) theories (e.g., CYC, SOAR, OSCAR), and there are theories framed with an eye toward implementability (e.g., predicate circumscription). Nevertheless, formal/theoretical work tends to focus on very narrow problems (and even on very special cases of very narrow problems) while trying to get them ``right'' in a very strict sense, while implementational work tends to aim at fairly broad ranges of behavior but often at the expense of any kind of overall conceptually unifying framework that informs understanding. It is sometimes urged that this gap is intrinsic to the topic: intelligence is not a unitary thing for which there will be a unifying theory, but rather a ``society'' of subintelligences whose overall behavior cannot be reduced to useful characterizing and predictive principles. Here we describe a formal architecture that is more closely tied to implementational constraints than is usual for formalisms, and which has been used to solve a number of commonsense problems in a unified manner. In particular, we address the issue of formal, integrated, and longitudinal reasoning: inferentially-modeled behavior that incorporates a fairly wide variety of types of commonsense reasoning within the context of a single extended episode of activity requiring keeping track of ongoing progress, and altering plans and beliefs accordingly. Instead of aiming at optimal solutions to isolated, well-specified and temporally narrow problems, we focus on satisficing solutions to under-specified and temporally-extended problems, much closer to real-world needs. We believe that such a focus is required for AI to arrive at truly intelligent mechanisms with the ability to behave effectively over considerably longer time periods and range of circumstances than is common in AI today. While this will surely lead to less elegant formalisms, it also surely is requisite if AI is to get fully out of the blocks-world and into the real world. (Also cross-referenced as UMIACS-TR-99-65

Calibrating, Counting, Grounding, Grouping

Even an ``elementary'' intelligence for control of the physical world will require very many kinds of knowledge and ability. Among these are ones related to perception, action, and reasoning about ``near space'': that region comprising one's body and the portion of space within reach of one's effectors; chief among these are individuation and categorization of objects. These in turn are made useful in part by the additional capacities to estimate category size, change one's beliefs about categories, and form new categories or revise old categories. In this position paper we point out some issues in knowledge representation that can arise with respect to the above capacities, and suggest that the framework of ``active logics'' (see below) may be marshaled toward solutions. We will conduct our discussion in terms of learning to understand in a semantically explicit way one's own sensori-motor system and its interactions with near-space objects. (Also cross-referenced as UMIACS-TR-94-63

Formal Real-Time Imagination

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,..