Jaques P.A.,PIPCA UNISINOS |
Seffrin H.,PIPCA UNISINOS |
Rubi G.,PIPCA UNISINOS |
De Morais F.,PIPCA UNISINOS |
And 3 more authors.
Expert Systems with Applications | Year: 2013
In order for an Intelligent Tutoring System (ITS) to correct students' exercises, it must know how to solve the same type of problems that students do and the related knowledge components. It can, thereby, compare the desirable solution with the student's answer. This task can be accomplished by an expert system. However, it has some drawbacks, such as an exponential complexity time, which impairs the desirable real-time response. In this paper we describe the expert system (ES) module of an Algebra ITS, called PAT2Math. The ES is responsible for correcting student steps and modeling student knowledge components during equations problem solving. Another important function of this module is to demonstrate to students how to solve a problem. In this paper, we focus mainly on the implementation of this module as a rule-based expert system. We also describe how we reduced the complexity of this module from O(n d) to O(d), where n is the number of rules in the knowledge base, by implementing some meta-rules that aim at inferring the operations students applied in order to produce a step. We evaluated our approach through a user study with forty-three seventh grade students. The students who interacted with our tool showed statistically higher scores on equation solving tests, after solving algebra exercises with PAT2Math during an approximately two-hour session, than students who solved the same exercises using only paper and pencil. © 2013 Elsevier Ltd. All rights reserved.
Lopes J.,Federal University of Rio Grande do Sul |
Souza R.,Federal University of Rio Grande do Sul |
Gadotti G.,LDAS FAEM UFPel |
Gusmao M.,PPGC CDTec UFPel |
And 4 more authors.
38th Latin America Conference on Informatics, CLEI 2012 - Conference Proceedings | Year: 2012
Ubiquitous computing (Ubicomp) environments are characterized by high distribution, heterogeneity and dynamism. In these environments, applications must be aware of their contexts and adapt to changes in them. A major research challenge in the area of Ubicomp is related to context awareness. Considering the characteristics of ubiquitous environments, this paper presents an architecture for context awareness, called DynamiCC (Dynamic Context Composition), that includes elements to support: context modeling, contextual data collection, actuation on the environment, and composition and interpretation of contextual information. We consider that the main contributions of this work are the employ of a hybrid approach for context modeling, and the proposal of an architecture that supports the interpretation and the composition of dynamic contexts, which enables the construction of complex contexts, in runtime of applications. To assess the functionality of the DynamiCC, we did a discussion of usage scenarios, highlighting the prototypes and tests performed. © 2012 IEEE.
Marcondes Filho D.,Federal University of Rio Grande do Sul |
Fogliatto F.S.,Federal University of Rio Grande do Sul |
de Oliveira L.P.L.,PIPCA UNISINOS
Producao | Year: 2011
Industrial batch processes are widely used in the production of certain items. Such processes provide a peculiar data structure; therefore there is a growing interest in the development of customized multivariate control charts for their monitoring. We investigate a recent approach that uses control charts based on the Statis method. Statis is an exploratory technique for measuring similarities between data matrices. However, the technique only assesses similarities in a linear context, i.e. investigating structures of linear correlation in the data. In this paper we propose control charts based on the Statis method in conjunction with a kernel for monitoring processes in the presence of strong nonlinearities. Through kernels we define nonlinear functions of data for better representing the structure to be characterized by the Statis method. The new approach, named Kernel-Statis, is developed and illustrated using simulated data.
De Araujo D.A.,PIPCA UNISINOS |
Rigo S.J.,PIPCA UNISINOS |
Muller C.,PPGLA UNISINOS |
Chishman R.,PPGLA UNISINOS
Proceedings - 2013 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IATW 2013 | Year: 2013
In this paper we present a novel methodology for automatic information extraction from natural language texts, based on the integration of linguistic rules, multiple ontologies and inference resources, integrated with an abstraction layer for linguistic annotation and data representation. The methodology allows ontology population with instances of events. The main contribution presented is related to the exploration of the flexibility of linguistic rules and domain knowledge representation, through their manipulation and integration by a reasoning system. The results from the case study indicate that the proposed approach is effective for the legal domain. © 2013 IEEE.
Gluz J.C.,PIPCA UNISINOS |
Jaques P.A.,PIPCA UNISINOS
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015
The BDI (Belief-Desire-Intention) model is a well known reasoning architecture for intelligent agents. According to the original BDI approach, an agent is able to deliberate about what action to do next having only three main mental states: belief, desires and intentions. A BDI agent should be able to choose the more rational action to be done with bounded resources and incomplete knowledge in an acceptable time. As humans need emotions to make immediate decisions with incomplete information, some recent works have extending the BDI architecture in order to integrate emotions. However, as they only use logic to represent emotions, they are not able to define the intensity of the emotions. In this paper we present an implementation of the appraisal process of emotions into BDI agents using a BDI language that integrates logic and probabilistic reasoning. Hence, our emotional BDI implementation allows to differentiate between emotions and affective reactions. This is an important aspect because emotions tend to generate stronger response. Besides, the emotion intensity also determines the intensity of an individual reaction. In particular, we implement the event-based emotions with consequences for self based on the OCC cognitive psychological theory of emotions. We also present an illustrative scenario and its implementation. © Springer International Publishing Switzerland 2015.
Seffrin H.M.,PIPCA UNISINOS |
Rubi G.L.,PIPCA UNISINOS |
Jaques P.A.,PIPCA UNISINOS
Proceedings of the ACM Symposium on Applied Computing | Year: 2014
An Intelligent Tutoring System (ITS) is an educational software that provides personal assistance for students, allowing them to learn at their own pace. This is possible because ITSs are able to map the learners' knowledge to create a student model. Most of the tutors use a Bayesian Network (BN) to perform this task, due to their ability to deal with uncertain data. However, classic static BNs are unable to model data, such as the student's knowledge, that changes over time. Dynamic Bayesian Networks (DBN) are an interesting option in this case, because they are a special type of BN that reasons over time. This paper presents an architecture of DBN that aims at inferring student's algebraic knowledge. This network was constructed based on a concept map, which was developed with the goal of structuring the algebraic knowledge, i. e. defining relationships among concepts. The proposed DBN was evaluated with the help of an expert in order to verify the ability of the network to predict the student's knowledge on the application of operations to solve 1st degree equations. This DBN is being integrated into an web-based ITS for algebra learning. Copyright 2014 ACM.