Data Mining - Concepts and Techniques.

From the Publisher:
An agent is an entity with domain knowledge, goals and actions. Multi-agent systems are a set of agents which
interact in a common environment. Multi-agent systems deal with the construction of complex systems involving multiple agents and their coordination. A multi-agent system (MAS) is a distributed computing system with
autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.

Introduction to Multiagent Systems

This paper introduces concepts and algorithms of feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms with a categorizing framework based on search strategies, evaluation criteria, and data mining tasks, reveals unattempted combinations, and provides guidelines in selecting feature selection algorithms. With the categorizing framework, we continue our efforts toward-building an integrated system for intelligent feature selection. A unifying platform is proposed as an intermediate step. An illustrative example is presented to show how existing feature selection algorithms can be integrated into a meta algorithm that can take advantage of individual algorithms. An added advantage of doing so is to help a user employ a suitable algorithm without knowing details of each algorithm. Some real-world applications are included to demonstrate the use of feature selection in data mining. We conclude this work by identifying trends and challenges of feature selection research and development.

/pdf/toward-integrating-feature-selection-algorithms-for-3c864gdey1.pdf

Toward integrating feature selection algorithms for classification and clustering

The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques. Of course, a single chapter cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.

https://www.informatica.si/index.php/informatica/article/download/148/140

Supervised Machine Learning: A Review of Classification Techniques

Transactions of the Institute of British Geographers

Modelling human tutors' socio-linguistic abilities computationally is necessary to underpin human-computer educational interactions with best pedagogic practices and to enhance the naturalness of such interactions. In this paper we present a computational model of tutorial feedback selection based on the context of the immediate situation for which the feedback is selected as well as on politeness considerations shown to be of importance to increasing pedagogical efficacy of computer assisted learning (Wang et al., 2008). The model is based on empirical studies with human tutors and on extensive linguistic analysis of human tutorial dialogues collected with the specific aim to inform the implementation of a natural language tutorial dialogue interface. The evaluation of the model, involving the comparison of its output with the linguistic responses produced by a human tutor, demonstrates the model's plausibility and highlights future directions for improving natural language human-computer interactions for educational purposes.

Modelling human tutors' feedback to inform natural language interfaces for learning

This paper introduces an approach to representing the kinds of information that components in a natural language generation (NLG) system will need to communicate to one another. This information may be partial, may involve more than one level of analysis and may need to include information about the history of a derivation. We present a general representation scheme capable of handling these cases. In addition, we make a proposal for organising intermodule communication in an NLG system by having a central server for this information. We have validated the approach by a reanalysis of an existing NLG system and through a full implementation of a runnable specification.

A Representation for Complex and Evolving Data Dependencies in Generation

So far, there has been little success in Natural Language Generation in coming up with general models of the content selection process. Nonetheless, there has been some work on content selection that employ Machine learning or heuristic search. On the other side, there is a clear tendency in NLG towards the use of resources encoded in standard Semantic Web representation formats. For these reasons, we believe that time has come to propose an initial challenge on content selection from Semantic Web data. In this paper, we briefly outline the idea and plan for the execution of this task.

Content Selection From Semantic Web Data

This paper addresses two previously unresolved issues in the automatic evaluation of Text Structuring (TS) in Natural Language Generation (NLG). First, we describe how to verify the generality of an existing collection of sentence orderings defined by one domain expert using data provided by additional experts. Second, a general evaluation methodology is outlined which investigates the previously unaddressed possibility that there may exist many optimal solutions for TS in the employed domain. This methodology is implemented in a set of experiments which identify the most promising candidate for TS among several metrics of coherence previously suggested in the literature.1

Using a Corpus of Sentence Orderings Defined by Many Experts to Evaluate Metrics of Coherence for Text Structuring

This paper experimentally shows that mixed-initiative dialogue is not always more efficient than non-mixed initiative dialogue in route finding tasks. Based on the dialogue model proposed in Conversation Analysis and Discourse Analysis a la the Birmingham school and Whittaker and Stenton‘s definition of initiative, we implement dialogue systems and obtain experimental results by making the systems interact with each other. Across a variety of instantiations of the dialogue model, the results show that with easy problems, the efficiency of mixed-initiative dialogue is a little better than or equal to that of non-mixed-initiative dialogue, while with difficult problems mixed-initiative dialogue is less efficient than non-mixed- initiative dialogue.

Chris Mellish

Papers

Modelling human tutors' feedback to inform natural language interfaces for learning

A Representation for Complex and Evolving Data Dependencies in Generation

Content Selection From Semantic Web Data

Using a Corpus of Sentence Orderings Defined by Many Experts to Evaluate Metrics of Coherence for Text Structuring

Exploring Mixed-Initiative Dialogue Using Computer Dialogue Simulation