Learning goal hierarchies from structured observations and expert annotations

We describe a relational learning by observation framework that automatically creates cognitive agent programs that model expert task performance in complex dynamic domains. Our framework uses observed behavior and goal annotations of an expert as the primary input, interprets them in the context of background knowledge, and returns an agent program that behaves similar to the expert. We map the problem of creating an agent program on to multiple learning problems that can be represented in a “supervised concept learning’’ setting. The acquired procedural knowledge is partitioned into a hierarchy of goals and represented with first order rules. Using an inductive logic programming (ILP) learning component allows our framework to naturally combine structured behavior observations, parametric and hierarchical goal annotations, and complex background knowledge. To deal with the large domains we consider, we have developed an efficient mechanism for storing and retrieving structured behavior data. We have tested our approach using artificially created examples and behavior observation traces generated by AI agents. We evaluate the learned rules by comparing them to hand-coded rules. Editor: Rui Camacho     

The process of introducing information technology

Abstract

This paper outlines the strategies adopted by the psychologists and ergonomists of the HUSAT Research Group to help organizations learn about information technology and systematically review and plan its organizational ramifications. An essential point is that it is not enough to understand the technology; effective implementation demands the ability to establish organizational needs and to choose a form of technology which will meet them.

The paper examines three ways of designing systems. Firstly, a technology-led approach which leads to 'fire fighting' when the negative organizational effects become apparent. A second method has tried to compensate for this by involving users in the design process. Unfortunately by the time the users have come to terms with their new task and are able to make a contribution, the system has usually been designed.

The third method of design expressly seeks to give users the time and opportunity to learn how to contribute to design, by making the design process evolutionary; i.e. by building slowly from small systems to large ones and retaining the flexibility to change. Within this concept user learning and adaptation is promoted by pilot systems, user design exercises, user support and evaluation procedures. It is only by these methods that users can be given the confidence and knowledge to exploit the potential of the new technology.     

Customising grammar and style checker rules

Abstract

In order to make grammar and style checkers customizable to meet writers' individual or organisational house style needs, complex rules specifying how to recognise and replace undesirable forms must be modified by non-expert users. Attempts in current commercial systems to provide such a facility are unsatisfactory: given the notations used to represent rules in these systems, any system that is powerful enough to perform its basic task of grammar and style checking is too complex to be comprehensible to a rule writer. This paper argues that any system with adequate natural language processing (NLP) resources to perform the basic tasks of a grammar and style checker can be augmented with a rule definition facility which, largely making use of those same resources, would be radically more usable than any existing system. The proposed approach is crucially dependent on the modular representation of system knowledge and incorporates techniques from knowledge representation, human-computer interaction and machine learning.     

Learning expert systems by being corrected

This paper describes a new method of knowledge acquisition for expert systems. A program, KABCO, interacts with a domain expert and learns how to make examples of a concept. This is done by displaying examples based upon KABCO's partial knowledge of the domain and accepting corrections from the expert. When the expert judges that KABCO has learnt the domain completely a large number of examples are generated and given to a standard machine learning program that learns the actual expert system rules. KABCO vastly eases the task of constructing an expert system using machine learning programs because it allows expert system rule bases to be learnt from a mixture of general (rules) and specific (examples) information. At present KABCO can only be used for classification domains but work is proceedings to extend it to be useful for other domains. KABCO learns disjunctive concepts (represented by frames) by modifying an internal knowledge base to remain consistent with all the corrections that have been entered by the expert. KABCO's incremental learning uses the deductive processes of modification, exclusion, subsumption and generalization. The present implementation is primitive, especially the user interface, but work is proceeding to make KABCO a much more advanced knowledge engineering tool.     

Context-aware agents for user-oriented web services discovery and execution

Service-oriented computing (SOC) suggests that the Internet will be an open repository of many modular capabilities realized as web services. Organizations may be able to leverage this SOC paradigm if their employees are able to ubiquitously incorporate such capabilities and their resulting information into their daily practices. It is impractical to assume that human users will be able to manually search vast distributed repositories at real-time. This paper presents an architecture, Software Agent-Based Groupware using E-services (SAGE), that incorporates the use of intelligent agents to integrate human users with web services. SAGE provides background search and discovery approaches, thus enabling human users to exploit service-based capabilities that were previously too time-consuming to locate and integrate. We present a multi-agent system where each agent learns the rule-based preferences of a human user with regards to their current operational “context” and manages the incorporation of relevant web services. Recommended by: Djamal Benslimane and Zakaria Maamar     

Machine learning for map interpretation: An intelligent tool for environmental planning

The design of a user interface integrating instruments for visual and textual representation and image interpretation is a relevant problem when developing an advisory system for environmental planning. Indeed, the user of the system needs a support to the interpretation of maps, that is, a tool that segments maps and automatically associates geometric regions on a map with those semantic labels useful for applying hints and advices suggested by the environmental planning system. In the article, we present the application of symbolic machine learning techniques to the interpretation of maps. Two inductive learning systems, namely, INDUBI/CSL and ATRE, have been used to complete the knowledge base of an expert system for environmental planning. The application described concerns the recognition of four environmental concepts that are relevant for environmental protection. The positive results obtained in two different experiments prove the strength of the adopted approach for the interpretation task.     

User profiling for the melvil knowledge retrieval system

Melvil is an ontology-based knowledge retrieval platform that provides a three-dimensional visualization of search results. The user can tailor the presentation of the search results to his or her preferences by changing the settings of various parameters on the screen. In this paper, we report on a prototype implementation of a user profiling device that learns to predict appropriate settings for these parameters for the current search results based on previous experiences. In a preliminary study, we evaluated several off-the-shelf machine learning algorithms on parts of the problem. The final implementation required the flexibility of handling both regression and classification problems, being able to deal with set-valued input and output attributes, as well as incorporating Melvil 's ontologies for the respective application domain. Thus, we selected a nearest-neighbor approach for the prototype implementation. An evaluation on off-line data collected from several users showed a satisfactory performance.     

Case-based analysis in user requirements modelling for knowledge construction

ContextLearning can be regarded as knowledge construction in which prior knowledge and experience serve as basis for the learners to expand their knowledge base. Such a process of knowledge construction has to take place continuously in order to enhance the learners’ competence in a competitive working environment. As the information consumers, the individual users demand personalised information provision which meets their own specific purposes, goals, and expectations.ObjectivesThe current methods in requirements engineering are capable of modelling the common user’s behaviour in the domain of knowledge construction. The users’ requirements can be represented as a case in the defined structure which can be reasoned to enable the requirements analysis. Such analysis needs to be enhanced so that personalised information provision can be tackled and modelled. However, there is a lack of suitable modelling methods to achieve this end. This paper presents a new ontological method for capturing individual user’s requirements and transforming the requirements onto personalised information provision specifications. Hence the right information can be provided to the right user for the right purpose.MethodAn experiment was conducted based on the qualitative method. A medium size of group of users participated to validate the method and its techniques, i.e. articulates, maps, configures, and learning content. The results were used as the feedback for the improvement.ResultThe research work has produced an ontology model with a set of techniques which support the functions for profiling user’s requirements, reasoning requirements patterns, generating workflow from norms, and formulating information provision specifications.ConclusionThe current requirements engineering approaches provide the methodical capability for developing solutions. Our research outcome, i.e. the ontology model with the techniques, can further enhance the RE approaches for modelling the individual user’s needs and discovering the user’s requirements.     

A statistical recommendation model of mobile services based on contextual evidences

Mobile devices are undergoing great advances in recent years allowing users to access an increasing number of services or personalized applications that can help them select the best restaurant, locate certain shops, choose the best way home or rent the best film. However this great quantity of services does not require the user to find and select those services needed for each specific situation. The classical approaches link some preferences to certain services, include the recommendations given by other users or even include certain fixed rules in order to choose the most appropriate services. However, since these methods assume that user needs can be modelled by fixed rules or preferences, they fail when modelling different users or makes them difficult to train. In this paper we propose a new algorithm that learns from the user’s actions in different contextual situations, which allows to properly infer the most appropriate recommendations for a user in a specific contextual situation. This model, by using of a double knowledge diffusion approach, has been specifically designed to face the inherent lack of learning evidences, computational cost and continuous training requirements and, therefore, overcomes the performance and convergence rates offered by other learning methodologies.     

Finite element mesh design expert system

The paper presents a consultative rule-based expert system for finite element mesh design. The aim of the expert system presented is to propose the appropriate type of the finite elements and determine the resolution values for the finite element mesh to be used for the analysis. The extensive knowledge base, comprising about 1900 rules, was built mainly by the use of machine learning (ML) techniques. Several examples will confirm that an expert system shell written in Prolog enables efficient use of the knowledge base and adequate communication between the system and the user. The system has the ability to explain the inference process. Thus, it can also be used as a teaching tool for inexperienced users—students. The results of the experimental use of the system are encouraging and can be used as guidelines for further developments and improvements of the system.     

A Reduction Algorithm Meeting Users Requirements

Generally a database encompasses various kinds of knowledge and is shared by many users.Different users may prefer different kinds of knowledge.So it is important for a data mining algorithm to output specific knowledge according to users‘ current requirements (preference).We call this kind of data mining requirement-oriented knowledge discovery (ROKD).When the rough set theory is used in data mining,the ROKD problem is how to find a reduct and corresponding rules interesting for the user.Since reducts and rules are generated in the same way,this paper only concerns with how to find a particular reduct.The user‘s requirement is described by an order of attributes,called attribute order,which implies the importance of attributes for the user.In the order,more important attributes are located before less important ones.then the problem becomes how to find a reduct including those attributes anterior in the attribute order.An approach to dealing with such a problem is proposed.And its completeness for reduct is proved.After that,three kinds of attribute order are developed to describe various user requirements.     

Usability and database search at the Swedish Employment Service

In this paper we report on a large-scale developmental project (AIS-95, 12,000 users) at the Swedish Labor Market Administration. The project is analyzed from the point of view of achieving good effective productivity in the final work system. Developmental projects of large size pose special problems in this respect. The aim of the project was to replace the current database application system (AF-90, 8000 users) with an improved system. As a background, we first analyzed the problems the employees have when using the current system (AF-90) from the perspectives of functionality, usability and information needs. No systematic attempt was made in the development project to integrate knowledge about users' problems with the AF-90 project into the AIS-95 project. Furthermore, different forms of user participation were tried in the project but still many users reported feeling a lack of influence on the project. Conclusions are given with respect to how some of the deficiencies found in the present project might be avoided in future large-scale projects.     

Teachers Constructing Concepts of Mathematics for Teaching and Learning: “It's like the roots beneath the surface,not a bigger garden”

Abstract

Conceptually rich classroom learning environments can only be supported by teachers with appropriate mathematical knowledge. A lack of clarity exists as to whether or how such teacher knowledge might go beyond knowledge of the relevant curriculum. This study contributes to the field by investigating further examples of what appropriate teacher mathematical knowledge might be, as rooted and contextualized in teachers’ daily classroom practices. Teacher journaling, individual meetings, and teacher focus-group discussions were used to identify relevant examples, and ultimately continue to collectively describe, in a specific, contextually based and practitioner-developed manner, the mathematical knowledge required for elementary teaching.     

A methodological approach for designing and evaluating intelligent applications for digital collections

The rapid evolution of Internet services has led to a constantly increasing number of Web sites and to an increase in the available information. The main challenge is to support Web users in order to facilitate navigation through Web sites and to improve searching among the extremely large Web repository, such as digital libraries, online product catalogues, or other generic information sources. The complexity of today's services could be lowered by means of proactive support or advice from the system. The proactiveness could be achieved using dialoguing agents that exploit user profiles to provide personal recommendations. In this paper, we will present a general methodology to cover the entire process of designing advanced solutions for online services. The methodology has been adopted to elicit user requirements for the system developed in the COGITO project, and to evaluate the performance of the final prototype.     

User Modeling in Spoken Dialogue Systems to Generate Flexible Guidance

We address the issue of appropriate user modeling to generate cooperative responses to users in spoken dialogue systems. Unlike previous studies that have focused on a user’s knowledge, we propose more generalized modeling. We specifically set up three dimensions for user models: the skill level in use of the system, the knowledge level about the target domain, and the degree of urgency. Moreover, the models are automatically derived by decision tree learning using actual dialogue data collected by the system. We obtained reasonable accuracy in classification for all dimensions. Dialogue strategies based on user modeling were implemented on the Kyoto City Bus Information System that was developed at our laboratory. Experimental evaluations revealed that the cooperative responses adapted to each subject type served as good guides for novices without increasing the duration dialogue lasted for skilled users.     

Balanced Cooperative Modeling

Machine learning techniques are often used for supporting a knowledge engineer in constructing a model of part of the world. Different learning algorithms contribute to different tasks within the modeling process. Integrating several learning algorithms into one system allows it to support several modeling tasks within the same framework. In this article, we focus on the distribution of work between several learning algorithms on the one hand and the user on the other hand. The approach followed by the MOBAL system is that ofbalanced cooperation, i.e., each modeling task can be done by the user or by a learning tool of the system. The MOBAL system is described in detail. We discuss the principle of multi-functionality of one representation for the balanced use by learning algorithms and users.     

Intelligent Travel Planning: A MultiAgent Planning System to Solve Web Problems in the e-Tourism Domain

This paper presents Intelligent Travel Planning (ITP), a multiagent planning system to solve Web electronic problems in the Web, whose main goal is to search for useful solutions in the electronic-Tourism domain to system users. The system uses different types of intelligent autonomous agents whose main characteristics are cooperation, negotiation, learning, planning and knowledge sharing. Obviously the information used by the intelligent agents is heterogeneous and geographically distributed, since the main information source of the system is Internet. Other information sources are agent knowledge bases in the distributed system. The process to obtain, filter, and store the information is performed automatically by agents. This information is translated into a homogeneous format for high-level reasoning in order to obtain different partial solutions. Partial solutions are reconstructed into a general solution (or solutions) to be presented to the user. The system will show a set of solutions to the users that can be evaluated by them.