THE "POWER" OF TEXT PRODUCTION ACTIVITY IN COLLABORATIVE MODELING : NINE RECOMMENDATIONS TO MAKE A COMPUTER SUPPORTED SITUATION WORK

Language is not a direct translation of a speaker’s or writer’s knowledge or intentions. Various complex processes and strategies are involved in serving the needs of the audience: planning the message, describing some features of a model and not others, organizing an argument, adapting to the knowledge of the reader, meeting linguistic constraints, etc. As a consequence, when communicating about a model, or about knowledge, there is a complex interaction between knowledge and language. In this contribution, we address the question of the role of language in modeling, in the specific case of collaboration over a distance, via electronic exchange of written textual information. What are the problems/dimensions a language user has to deal with when communicating a (mental) model? What is the relationship between the nature of the knowledge to be communicated and linguistic production? What is the relationship between representations and produced text? In what sense can interactive learning systems serve as mediators or as obstacles to these processes

Learning Features that Predict Cue Usage

Our goal is to identify the features that predict the occurrence and placement of discourse cues in tutorial explanations in order to aid in the automatic generation of explanations. Previous attempts to devise rules for text generation were based on intuition or small numbers of constructed examples. We apply a machine learning program, C4.5, to induce decision trees for cue occurrence and placement from a corpus of data coded for a variety of features previously thought to affect cue usage. Our experiments enable us to identify the features with most predictive power, and show that machine learning can be used to induce decision trees useful for text generation.Comment: 10 pages, 2 Postscript figures, uses aclap.sty, psfig.te

Folk Psychology and the Bayesian Brain

Whilst much has been said about the implications of predictive processing for our scientific understanding of cognition, there has been comparatively little discussion of how this new paradigm fits with our everyday understanding of the mind, i.e. folk psychology. This paper aims to assess the relationship between folk psychology and predictive processing, which will first require making a distinction between two ways of understanding folk psychology: as propositional attitude psychology and as a broader folk psychological discourse. It will be argued that folk psychology in this broader sense is compatible with predictive processing, despite the fact that there is an apparent incompatibility between predictive processing and a literalist interpretation of propositional attitude psychology. The distinction between these two kinds of folk psychology allows us to accept that our scientific usage of folk concepts requires revision, whilst rejecting the suggestion that we should eliminate folk psychology entirely

Technological Threat Attribution, Trust and Confidence, and the Contestability of National Security Policy

The world has been asked to believe that China is a source of cyberthreat and that Russia is meddling in U.S. elections. Western populations are being asked to trust the words of intelligence agencies and world leaders that these unspecified technological threats are real. The oftenclassified nature of the threat results in governments not being able to provide the public with an evidence base for the threat attribution. This presents a social scientific crisis where without substantive evidence the public is asked to trust and have confidence in a particular technological threat attribution claim without any further assurance. It is sensible for the public to ask whose security claim should be believed and why? Likewise, it seems a critical social responsibility for security policy makers and academia to first acknowledge this conundrum and then strive to develop frameworks to better understand the trust and confidence challenges around technological threat attribution. This talk draws on New Zealand as a sociological case study to illustrate where and if a technological threat attribution and trust and confidence challenge might be evident in the Department of Prime Minister and Cabinet’s 2018 National Cyber Strategy refresh and the New Zealand Defence Force’s 2018 Strategic Defense Policy Statement. This case study is used to sketch out a broader project focusing on how the contestability of national security strategy and government security discourse can present specific trust and confidence challenges for both the public and government, and how we might begin to address these challengesfals

A Plan-Based Model for Response Generation in Collaborative Task-Oriented Dialogues

This paper presents a plan-based architecture for response generation in collaborative consultation dialogues, with emphasis on cases in which the system (consultant) and user (executing agent) disagree. Our work contributes to an overall system for collaborative problem-solving by providing a plan-based framework that captures the {\em Propose-Evaluate-Modify} cycle of collaboration, and by allowing the system to initiate subdialogues to negotiate proposed additions to the shared plan and to provide support for its claims. In addition, our system handles in a unified manner the negotiation of proposed domain actions, proposed problem-solving actions, and beliefs proposed by discourse actions. Furthermore, it captures cooperative responses within the collaborative framework and accounts for why questions are sometimes never answered.Comment: 8 pages, to appear in the Proceedings of AAAI-94. LaTeX source file, requires aaai.sty and epsf.tex. Figures included in separate file

Plan-based delivery composition in intelligent tutoring systems for introductory computer programming

In a shell system for the generation of intelligent tutoring systems, the instructional model that one applies should be variable independent of the content of instruction. In this article, a taxonomy of content elements is presented in order to define a relatively content-independent instructional planner for introductory programming ITS's; the taxonomy is based on the concepts of programming goals and programming plans. Deliveries may be composed by the instantiation of delivery templates with the content elements. Examples from two different instructional models illustrate the flexibility of this approach. All content in the examples is taken from a course in COMAL-80 turtle graphics