Jennifer J. Elgot-Drapkin

Reasoning situated in time I: basic concepts

The needs of a real-time reasoner situated in an environment may make it appropriate to view error-correction and non-monotonicity as much the same thing. This has led us to formulate situated (or step) logic, an approach to reasoning in which the formalism has a kind of real-time self-reference that affects the course of deduction itself. Here we seek to motivate this as a useful vehicle for exploring certain issues in commonsense reasoning. In particular, a chief drawback of more traditional logics is avoided: from a contradiction we do not have all wffs swamping the (growing) conclusion set. Rather, we seek potentially inconsistent, but nevertheless useful, logics where the real-time self-referential feature allows a direct contradiction to be spotted and corrective action taken, as part of the same system of reasoning. Some specific inference mechanisms for real-time default reasoning are suggested, notably a form of introspection relevant to default reasoning. Special treatment of ‘‘now’’ and of contradictions are the main technical devices here. We illustrate this with a computer-implemented real-time solution to R. Moore’s Brother Problem.

LIFE ON A DESERT ISLAND: ONGOING WORK ON REAL-TIME REASONING

ABSTRACT We discuss ongoing work in real-time reasoning with applications to default reasoning. We explore ways in which this may also be relevant to the frame problem and associated issues.

Stop the world—I want to think

Reason‐based actions plunge the reasoner into temporal considerations from all angles. We see this not only when time enters explicitly into the problem statement, but also in formal robot blocks‐world scenarios, in the Yale Shooting Problem, and other associated versions of the frame problem (e.g., Hanks and McDermott1), in various specialized actions (e.g., hiding, as in Allen2), and so on. In short, where there is action, there is time, and where there is time, there is a potential need for reasoning about time. Where, then, is the action? It certainly includes the usual overt physical acts of motion, and also certain covert behaviors such as hiding or watching. In these, of course, time is important. But there is another angle that is not usually noted, one that we have been exploring for the past several years3–6. Namely, action also occurs in the form of mere thinking or reasoning. Moreover, the very same temporal considerations apply to this reasoning behavior. This leads us to view reasoning itself as a kind of action, with the obvious yet nontrivial consequence that our reasoning goes on “as the world turns.” the present article offers various arguments in support of this position.

A flexible marker-passer for semantically weak search

New challenges posed in many areas of AI research represent a departure from domain-specific applications-to systems that can more effectively cope with larger and more uncertain domains. Such knowledge intensive applications require the easy and efficient utilization of tremendous amounts of knowledge. The magnitude of knowledge and often stringent response constraints that characterize such applications poses a computationally prohibitive search problem. A proposed technique for addressing this problem is a parallel search technique known as marker-passing. Past work in markerpassers has shown they often return too much information, becoming a bottleneck of the system in which they are embedded. This paper presents the design of a flexible marker-passing mechanism embedded in a commonsense reasoning model, RABIT (_Reasoning About Beliefs In Time), which overcomes this difficulty. The unique design we present avoids traditional drawbacks in markerpassing implementations by emphasizing search over inference as the goal of the marker-passing process. This marker-passing design is powerful due to its separation of the marker-passing process from the knowledge contained in the network itselL thus allowing for its potential use not only in the area of commonsense reasoning, but also in many other domains, including, but not limited to, natural language processing, general-purpose planning, and robot navigation.