Case-based reasoning using expert systems to determine electricity reduction in residential buildings

Case-based reasoning enables solving new problems using past experience, by reusing solutions for past problems. The simplicity of this technique has made it very popular in several domains. However, the use of this type of approach to support decisions in the power and energy domain is still rather unexplored, especially regarding the flexibility of consumption in buildings in response to recent environmental concerns and consequent governmental policies that envisage the increase of energy efficiency. In order to determine the amount of consumption reduction that should be applied in a building, this article proposes a methodology that adapts the past results of similar cases in order to achieve a decision for the new case. A clustering methodology is used to identify the most similar previous cases, and an expert system is developed to refine the final solution after the combination of the similar cases results. The proposed CBR methodology is evaluated using a set of real data from a residential building. Results prove the advantages of the proposed methodology, demonstrating its applicability to enhance house energy management systems by determining the amount of reduction that should be applied in each moment, thus allowing such systems to carry out the reduction through the different loads of the building.This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and a grant agreement No 703689 (project ADAPT); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013info:eu-repo/semantics/publishedVersio

An overview of memory: some issues on structures and organization in the legal domain

Lawyers often need to look for previous similar legal cases when analysing new ones. The more previous cases, the more time is spent. Classical search engines execute termbased retrieval, which may miss relevant documents as well as fetch several irrelevant ones, causing lack of useful information and waste of time. Ideally, retrieval should be meaning-based. Humans beings are able to do e cient searches due to their knowledge. Therefore, semantic search requires knowledge. This paper presents a semantic search engine. Along the paper, several issues concerning specially knowledge representation and memory are discussed. A formalism based on models of comprehension is introduced, as well as its motivation. Examples of representation of sentences in natural language from the Legal Domain are provided. The search engine and its architecture, based on domain knowledge, are brie y commented. The main goal is to give legal o ces the opportunity to save time by providing a more suitable document retrieval.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

An overview of memory: some issues on structures and organization in the legal domain

Lawyers often need to look for previous similar legal cases when analysing new ones. The more previous cases, the more time is spent. Classical search engines execute termbased retrieval, which may miss relevant documents as well as fetch several irrelevant ones, causing lack of useful information and waste of time. Ideally, retrieval should be meaning-based. Humans beings are able to do e cient searches due to their knowledge. Therefore, semantic search requires knowledge. This paper presents a semantic search engine. Along the paper, several issues concerning specially knowledge representation and memory are discussed. A formalism based on models of comprehension is introduced, as well as its motivation. Examples of representation of sentences in natural language from the Legal Domain are provided. The search engine and its architecture, based on domain knowledge, are brie y commented. The main goal is to give legal o ces the opportunity to save time by providing a more suitable document retrieval.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Recuperação inteligente de jurisprudência: uma avaliação do raciocínio baseado em casos aplicado a recuperação de jurisprudências no Tribunal Regional Eleitoral do Distrito Federal

Trata-se de uma pesquisa cujo objeto é avaliar a medida de precisão de um sistema de recuperação de informação jurídica (jurisprudência) que utiliza técnica de inteligência artificial conhecida como Raciocínio Baseado em Casos (RBC). Nesse modelo as jurisprudências são organizadas sob a forma de casos jurídicos concretos. O raciocínio baseado em casos tem como princípio a idéias de que um caso jurídico passado pode ser útil para resolver um problema atual, desde que exista entre eles algum grau de semelhança. Para estabelecer semelhanças entre casos atuais e passados o modelo estudado propõe o uso de cálculo de similaridade que é realizado com base na comparação de índices temáticos obtidos a partir do processo de indexação realizado por especialistas utilizando-se como apoio um tesauro jurídico. Esta pesquisa utiliza como universo as jurisprudências produzidas pelo Tribunal Regional Eleitoral do Distrito Federal. A amostra foi composta, considerando o recorte dado à pesquisa, por jurisprudências eleitorais produzidas nas eleições gerais de 2006 no Distrito Federal. Para realizar a avaliação do modelo, foi construído um protótipo do sistema de recuperação de informação baseado em casos. Em seguida, avaliou-se o protótipo quanto ao grau de precisão obtido no resultado de um conjunto de buscas. O método adotado para as avaliações foi o mesmo utilizado na Text REtrieval Conference (TREC) de 2007, tarefa Legal Track. Após a coleta dos dados foi elaborado um relatório discutindo a possibilidade do sistema de recuperação de informação baseado em casos ser considerado um paradigma para a recuperação de informação jurídica eleitoral

Case-based information retrieval

Available at INIST (FR), Document Supply Service, under shelf-number : RP 12541 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc