Knowledge Representation in Intelligent Collaborative Educational Systems

Abstract. In this paper, the concept of collaborative intelligent educational system is presented. Different knowledge representation models are compared in the context of their use in collaborative intelligent educational systems. Advantages of semantic networks for knowledge representation in such systems are described. M ain advantages of extended semantic networks are shown and a set of basic operations regarding them is drawn up

MASITS Methodology Supported Development of Agent Based Intelligent Tutoring System MIPITS

Līdz šim ir izstrādātas daudzas intelektuālas mācību sistēmas (IMS-as), kas e-apmācības sistēmām pievieno adaptivitāti un intelektu. Intelektuāli aģenti ir visai plaši lietoti IMS-u izstrādē dēļ tādām savām īpašībām kā modularitāte un dabīga intelektuālu mehānismu implementēšana. Tajā pašā laikā IMS-u izstrāde ir sarežģīta un šim procesam ir nepieciešams metodoloģisks atbalsts, lai nodrošinātu to, ka aģentos sakņotas IMS-as tiek pieņemtas kā industriāls risinājums. Raksts atspoguļo specifisku aģentos sakņotu IMS-u izstrādes metodoloģiju MASITS un MIPITS sistēmu, kas izstrādāta ar šo metodoloģiju. Sistēma ir izstrādāta kursam „Mākslīgā intelekta pamati”. Tā piedāvā mācību materiālus un praktiskus uzdevumus, kā arī sniedz atgriezenisko saiti par apmācāmā risinājumu, novērtējot apmācāmā zināšanas. Galvenais uzsvars MIPITS sistēmā ir uz praktisku uzdevumu risināšanu. Uzdevumi tiek pielāgoti apmācāmā zināšanu līmenim un apmācāmā prioritātēm par uzdevumu apjomu un praktiskumu. Sistēma piedāvā trīs veidu problēmas: testus, pārmeklēšanas algoritmu un divpersonu spēļu algoritmu realizācijas uzdevumus

MASITS Methodology Supported Development of Agent Based Intelligent Tutoring System MIPITS

Many Intelligent Tutoring Systems (ITS) have been developed to add adaptivity and intelligence to e-learning systems. Intelligent agents are widely used in ITSs due to their characteristics such as modularity and facilitation of intelligent mechanism implementation. At the same time development of agent based ITS is complicated and methodological support is needed to enable industrial adoption of agent based ITSs. The paper describes a specific agent based ITS development methodology, named MASITS, and MIPITS system developed with the methodology. The system is created for the course “Fundamentals of Artificial Intelligence”. It offers learning materials, provides practical problems and gives feedback to the learner about his/her solution evaluating his/her knowledge. The main focus of the system is on problem solving. The problems are adapted to the learner’s knowledge level and preferences about difficulty, size and practicality of problems. The system offers three types of problems: tests, state space search problems and two person games algorithm problems