Questions are asked and answered every day. Question answering (QA) technology aims to deliver the same facility online. It goes further than the more familiar search based on keywords (as in Google, Yahoo, and other search engines), in attempting to recognize what a question expresses and to respond with an actual answer. First, questions do not often translate into a simple list of keywords. For example, the question (1) Which countries did the pope visit in the 1960s? A much more complex set of keywords is needed in order to get anywhere close to the intended result, and experience shows that people will not learn how to formulate and use such sets. Second, QA takes responsibility for providing answers, rather than a searchable list of links to potentially relevant documents (web pages), highlighted by snippets of text that show how the query matched the documents. While this is not much of a burden when the answer appears in a snippet and further document access is unnecessary, QA technology aims to move this from being an accidental property of search to its focus. In keyword search and in much work to date on QA technology, the information seeking process has been seen as a one-shot affair: the user asks a question, and the system provides a satisfactory response. However, early work on QA (Section 1.1) did not make this assumption, and newly targeted applications are hindered by it: while a user may try to formulate a question whose answer is the information Question Answering 631 they want, they will not know whether they have succeeded until something has been returned for examination. If what is returned is unsatisfactory or, while not the answer, is still of interest, a user needs to be able to ask further questions that are understood in the context of the previous ones. For these target applications, QA must be part of a collaborative search process (Section 3.3). In the rest of this section, we give some historical background on QA systems (Section 1.1), on dialogue systems in which QA has played a significant role (Section 1.2), and on a particular QA task that has been a major driver of the field over the past 8 years (Section 1.3). Section 2 describes the current state of the art in QA systems, organized around the de facto architecture of such systems. Section 3 discusses some current directions in which QA is moving, including the development of interactive QA.

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D E PA R T M E N T O F E L E CT R IC A L ENGINEERING UNIVE RSITY O F ILLIN O IS C H A M P A IG N, IL L.

No more useful are the records from single electrodes thrust into the nervous felt, except where they show the impulses of blocked fibers or butted cells. We can tell the firing of single cells or fibers and measure their frequencies, but the chaos of synapsis is less neatly handled, and it is this chaos to which most of the pen-wiggles are attributed. There are three ways ' to get electricaL information about the central nervous system. The first two are indirect, and have been largely exploited. One is to examine the input-output relations of the whole structure: the other is to use emergent fibers as electrotonic probes of events affecting their intramedullary extensions. The results of the former method are ambiguous, without detailed knowledge of the anatomy and physical properties of junctions.

THE question I want to discuss is whether a mechanical chess player can outplay its designer (1). I don't say "beat" its designer; I say "outplay." I want to set aside all mechanical brains that beat their designer by sheer brute power of analysis. If the designer is a mediocre player, who can see only three moves ahead, let the machine be restricted until it, too, can see only three moves ahead. Let us assume that the machine cannot analyze the position right out and that it must make judgments.

I think this machine -may be of interest in view of its connection ...:h the problems of trial-and-error learning, forgetting and feedback _ ems. The maze can be changed _ any desired mantler by rearranging the partitions between the twen --:five squares. In the maze there is a sensing finger, which can feel the -.:titions of the maze as it comes against them. This finger is moved .- The goal is mounted on a pin which can be slipped into a jack _ any of the twenty-five squares.

It is no pure coincidence that an organic chemist rather than a computer specialist is writing this foreword.

Harold Shephard Samuel D. Thurman William T. Lake JOINDER OF CLAIMS, COUNTERCLAIMS, AND CROSS-COMPLAINTS: SUGGESTED REVISION OF THE CALIFORNIA PROVISIONS. Research in artificial intelligence, a branch of computer science, has illuminated our capacity to use computers to model human thought processes. In this Article we will argue that the time has come for serious interdisciplinary work between lawyers and computer scientists to explore the computer's potential in law. Interdisciplinary work between the lawyer and the computer scientist has floundered on the misconceptions that each has of the other's discipline. As a result, no one has yet attempted computer programs incorporating complex techniques of legal reasoning. Even efforts in legal information retrieval have been hampered by these misconceptions. In retrieval, lawyers have viewed the computer as, at most, a storehouse from which cases and statutes might be retrieved by skillfully designed indexing systems.

The nature of the business doesn't matter; in every business computers have made numerous changes in record keeping, process control, and decision-making. And there will be more. One of the most important trends in computing is making computers behave intelligently. The software underneath this intelligent behavior is called an expert system, sometimes also called a knowledgebased system, or knowledge system. An expert system is a computer program that reasons about a problem in much the same way, and with about the same performance, as specialists. This chapter is about the trend toward using expert systems: what it means, how it's possible, and how to think about it. There have been lead articles about this in Fortune, Business Week, and Newsweek; most Fortune-SOO companies are using expert systems; many are establishing research and development groups for them; even staid IBM is marketing expert systems tools and using them internally. Bruce G. Buchanan I 129 There are many reasons why companies want to build an expert system. Most of them are based on the premise that: Expertise is a scarce resource. And the corollary (by Murphy's Law): Even when there is enough expertise, it is never close enough to the person who needs it in a hurry. Because this is true, almost by definition, an expert system containing some of the knowledge of a company's specialists may have several benefits.. There are several examples of expert systems working in various problem areas. At present, they are used more as "intelligent assistants" than as replacements for technicians or experts. That is, they help people think through difficult problems and may provide suggestions about what to do, without taking over every aspect of the task. Although the problems are quite different they can be categorized into two major classes problems of interpretation and problems of construction. Interpretive problem examples include Schlumberger's Dipmeter Advisor, which replicates the expertise of some of their company-wide specialists who interpret data from clients' oil wells and then sell the expert system's interpretations around the world.

In this paper we discuss some reasons why temporal logic might not be suitable to model real life norms. To show this, we present a novel deontic logic contrary-to-duty/derived permission paradox based on the interaction of obligations, permissions and contrary-to-duty obligations. The paradox is inspired by real life norms.