Education in Accounting using an Interactive System

This paper represents a summary of a research report and the results of developing an educational software, including a multi-agent system for teaching accounting bases and financial accounting. The paper describes the structure of the multi-agent system, defined as a complex network of s-agents. Each s-agent contains 6 pedagogical agents and a coordinator agent. We have defined a new architecture (BeSGOTE) that extends the BDI architecture for intelligent agents and we have defined a mixing-up relation among the accounts, presenting the way in which it can be used for testing students.Computer Aided Education, Multi-Agent System, Artificial Intelligence, Accounting Education

Education in Accounting using an Interactive System

This paper represents a summary of a research report and the results of developing an educational software, including a multi-agent system for teaching accounting bases and financial accounting. The paper describes the structure of the multi-agent system, defined as a complex network of s-agents. Each s-agent contains 6 pedagogical agents and a coordinator agent. We have defined a new architecture (BeSGOTE) that extends the BDI architecture for intelligent agents and we have defined a mixing-up relation among the accounts, presenting the way in which it can be used for testing students.Computer Aided Education, Multi-Agent System, Artificial Intelligence, Accounting Education

Towards a goal-oriented agent-based simulation framework for high-performance computing

Currently, agent-based simulation frameworks force the user to choose between simulations involving a large number of agents (at the expense of limited agent reasoning capability) or simulations including agents with increased reasoning capabilities (at the expense of a limited number of agents per simulation). This paper describes a first attempt at putting goal-oriented agents into large agentbased (micro-)simulations. We discuss a model for goal-oriented agents in HighPerformance Computing (HPC) and then briefly discuss its implementation in PyCOMPSs (a library that eases the parallelisation of tasks) to build such a platform that benefits from a large number of agents with the capacity to execute complex cognitive agents.Peer ReviewedPostprint (author's final draft

Використання онтологій для персоніфікованого пошуку знань у природномовних текстів

Запропонований у роботі підхід до персоніфікації пошуку інформаційних ресурсів та інформаційних об’єктів, що базується на побудові та використанні тезаурусу задачі користувача, дозволяє використовувати знання щодо предметної області пошуку та структури інформаційних об’єктів, представлені за допомогою відповідних онтологій. Наведені визначення семантичного пошуку, його суб’єктів та компоненті дозволяють більш чітко формулювати проблеми, пов’язані з пошуком інформації у відкритому середовищі Web. Програмна реалізація запропонованого підходу підтверджує ефективність його практичного використання.Предложенный в работе подход к персонификации поиска информационных ресурсов и информационных объектов, который базируется на построении и использовании тезауруса задачи пользователя, позволяет использовать знания относительно предметной области поиска и структуры информационных объектов, представленные с помощью соответствующих онтологий. Приведенные определения семантического поиска, его субъектов и компонентов позволяют более четко формулировать проблемы, связанные с поиском информации в открытой среде Web. Программная реализация предложенного подхода подтверждает эффективность его практического использования.The paper analyzes the problems of search personalization of information resources and information objects which is based on the construction and use of user task thesaurus. This thesaurus allows the use of knowledge about search domain and structure of information objects represented by some appropriate ontologies. The definitions of semantic search, its subjects and components allow more articulate issues related to the information retrieval in the Web open environment. Software implementation of the proposed approach confirms the effectiveness of its practical use

Higher-level Knowledge, Rational and Social Levels Constraints of the Common Model of the Mind

In his famous 1982 paper, Allen Newell [22, 23] introduced the notion of knowledge level to indicate a level of analysis, and prediction, of the rational behavior of a cognitive articial agent. This analysis concerns the investigation about the availability of the agent knowledge, in order to pursue its own goals, and is based on the so-called Rationality Principle (an assumption according to which "an agent will use the knowledge it has of its environment to achieve its goals" [22, p. 17]. By using the Newell's own words: "To treat a system at the knowledge level is to treat it as having some knowledge, some goals, and believing it will do whatever is within its power to attain its goals, in so far as its knowledge indicates" [22, p. 13]. In the last decades, the importance of the knowledge level has been historically and system- atically downsized by the research area in cognitive architectures (CAs), whose interests have been mainly focused on the analysis and the development of mechanisms and the processes governing human and (articial) cognition. The knowledge level in CAs, however, represents a crucial level of analysis for the development of such articial general systems and therefore deserves greater research attention [17]. In the following, we will discuss areas of broad agree- ment and outline the main problematic aspects that should be faced within a Common Model of Cognition [12]. Such aspects, departing from an analysis at the knowledge level, also clearly impact both lower (e.g. representational) and higher (e.g. social) levels

Towards an Ontological Modelling of Preference Relations

Preference relations are intensively studied in Economics, but they are also approached in AI, Knowledge Representation, and Conceptual Modelling, as they provide a key concept in a variety of domains of application. In this paper, we propose an ontological foundation of preference relations to formalise their essential aspects across domains. Firstly, we shall discuss what is the ontological status of the relata of a preference relation. Secondly, we investigate the place of preference relations within a rich taxonomy of relations (e.g. we ask whether they are internal or external, essential or contingent, descriptive or nondescriptive relations). Finally, we provide an ontological modelling of preference relation as a module of a foundational (or upper) ontology (viz. OntoUML). The aim of this paper is to provide a sharable foundational theory of preference relation that foster interoperability across the heterogeneous domains of application of preference relations