Adaptive hypermedia is a new direction of research within the area of adaptive and user model-based interfaces. Adaptive hypermedia (AH) systems build a model of the individual user and apply it for adaptation to that user, for example, to adapt the content of a hypermedia page to the user's knowledge and goals, or to suggest the most relevant links to follow. AH systems are used now in several application areas where the hyperspace is reasonably large and where a hypermedia application is expected to be used by individuals with different goals, knowledge and backgrounds. This paper is a review of existing work on adaptive hypermedia. The paper is centered around a set of identified methods and techniques of AH. It introduces several dimensions of classification of AH systems, methods and techniques and describes the most important of them.

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Methods and techniques of adaptive hypermedia

Bayesian Reasoning. Introduction to Bayesian Networks. Inference in Bayesian Networks. Bayesian Network Applications. Bayesian Planning and Decision-Making. Bayesian Network Applications II. Learning Bayesian Networks I. Learning Bayesian Networks II. Causality vs. Probability. Knowledge Engineering with Bayesian Networks I. Knowledge Engineering with Bayesian Networks II. Application Software.

Bayesian artificial intelligence

This article aims to present a set of discourse structure relations that are easy to code and to develop criteria for an appropriate data structure for representing these relations. Discourse structure here refers to informational relations that hold between sentences in a discourse. The set of discourse relations introduced here is based on Hobbs (1985).We present a method for annotating discourse coherence structures that we used to manually annotate a database of 135 texts from the Wall Street Journal and the AP Newswire. All texts were independently annotated by two annotators. Kappa values of greater than 0.8 indicated good interannotator agreement.We furthermore present evidence that trees are not a descriptively adequate data structure for representing discourse structure: In coherence structures of naturally occurring texts, we found many different kinds of crossed dependencies, as well as many nodes with multiple parents. The claims are supported by statistical results from our hand-annotated database of 135 texts.

https://tedlab.mit.edu/tedlab_website/researchpapers/Wolf_&_Gibson_2005_CompLing.pdf

Representing Discourse Coherence: A Corpus-Based Study

One of the key factors for the acceptance of expert systems in real-world domains is the ability to explain their reasoning (Buchanan & Shortliffe, 1984; Henrion & Druzdzel, 1990). This paper describes the basic properties that characterise explanation methods and reviews the methods developed to date for explanation in Bayesian networks.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S026988890200019X

A review of explanation methods for Bayesian networks

Rhetorical Structure Theory (RST) is a theory of text organization that has led to areas of application beyond discourse analysis and text generation, its original goals. In this article, we review...

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Applications of Rhetorical Structure Theory

During argumentation, people persuade their audience using a variety of strategies, e.g., hypothetical reasoning, reasoning by cases and ordinary premise-to-goal arguments. In this paper, we offer an operational definition of the conditions for pursuing these strategies, and incorporate into a Bayesian argument-generation system a mechanism for proposing applicable argumentation strategies, generating specific arguments based on these strategies, and selecting a final argument.

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Using Argumentation Strategies in Automated Argument Generation

Our argumentation system, NAG, uses Bayesian networks in a user model and in a normative model to assemble and assess arguments which balance persuasiveness with normative correctness. Attentional focus is simulated in both models to select relevant subnetworks for Bayesian propagation. The subnetworks are expanded in an iterative abductive process until argumentative goals are achieved in both models, when the argument is presented to the user.

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Bayesian reasoning in an abductive mechanism for argument generation and analysis

In this paper, we describe a content planning mechanism which takes into consideration a user's possible inferences in order to generate the most concise discourse that achieves a given communicative goal. The consideration of a user's inferences results in the addition of information that addresses erroneous inferences , and the omission of easily inferred information. Given a communicative goal, our mechanism applies inference rules in backward reasoning mode to plan rhetorical devices that achieve this goal, and subsequently in forward reasoning mode to conjecture the eeect of these rhetorical devices on the user's beliefs. This process results in one or more sets of rhetorical devices which achieve the intended communicative goal. Each set is then minimized, and the most concise among these sets is selected. These ideas have been implemented in a system called WISHFUL, which generates explanations about concepts.

Generating Concise Discourse that Addresses a Users Inferences.

In order to argue effectively one must have a grasp of both the normative strength of the inferences that come into play and the effect that the proposed inferences will have on the audience. In this paper we describe a program,NAG(Nice Argument Generator), that attempts to generate arguments that are both persuasive and correct. To do so NAG incorporates two models: a normative model, for judging the normative correctness of an argument, and a user model, for judging the persuasive effect of the same argument upon the user. The user model incorporates some of the common errors humans make when reasoning. In order to limit the scope of its reasoning during argument evaluation and generation NAG explicitly simulates attentional processes in both the use r and the normative models.

A cognitive model of argumentation

We describe an interactive system which supports the exploration of arguments generated from Bayesian networks. In particular, we consider key features which support interactive behaviour: (1) an attentional mechanism which updates the activation of concepts as the interaction progresses; (2) a set of exploratory responses; and (3) a set of probabilistic patterns and an Argument Grammar which support the generation of natural language arguments from Bayesian networks. A preliminary evaluation assesses the effect of our exploratory responses on user's beliefs.

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Richard McConachy

Papers

Using Argumentation Strategies in Automated Argument Generation

Bayesian reasoning in an abductive mechanism for argument generation and analysis

Generating Concise Discourse that Addresses a Users Inferences.

A cognitive model of argumentation

Exploratory Interaction with a Bayesian Argumentation System