Spelling suggestions: "subject:"[een] INTELLIGENT AGENT"" "subject:"[enn] INTELLIGENT AGENT""
31 |
A Logical Theory of Joint Ability in the Situation CalculusGhaderi, Hojjat 17 February 2011 (has links)
Logic-based formalizations of dynamical systems are central to the field of knowledge representation and reasoning. These formalizations can be used to model agents that act, reason,and perceive in a changing and incompletely known environment. A key aspect of reasoning about agents and their behaviors is the notion of joint ability. A team of agents is jointly able
to achieve a goal if despite any incomplete knowledge or even false beliefs about the world or each other, they still know enough to be able to get to a goal state, should they choose to do so. A particularly challenging issue associated with joint ability is how team members can coordinate their actions. Existing approaches often require the agents to communicate to agree
on a joint plan. In this thesis, we propose an account of joint ability that supports coordination among agents without requiring communication, and that allows for agents to have incomplete (or even false) beliefs about the world or the beliefs of other agents. We use ideas from game theory to address coordination among agents. We introduce the notion of a strategy for each
agent which is basically a plan that the agent knows how to follow. Each agent compares her strategies and iteratively discards those that she believes are not good considering the strategies that the other agents have kept. Our account is developed in the situation calculus, a logical language suitable for representing and reasoning about action and change that is extended to support reasoning about multiple agents. Through several examples involving public, private, and sensing actions, we demonstrate how symbolic proof techniques allow us to reason about team ability despite incomplete specifications about the beliefs of agents.
|
32 |
[en] HIERARCHICAL NEURAL FUZZY MODELS BASED ON REINFORCEMENT LEARNING OF INTELLIGENT AGENTS / [pt] MODELOS NEURO-FUZZY HIERÁRQUICOS COM APRENDIZADO POR REFORÇO PARA MULTI-AGENTES INTELIGENTESMARCELO FRANCA CORREA 20 February 2013 (has links)
[pt] Os benefícios trazidos pela aplicação de Sistemas Multi-Agentes (SMA) são
diversos. Através da computação paralela, agentes podem trabalhar em conjunto para
explorar melhor a estrutura descentralizada de uma determinada tarefa e acelerar sua
conclusão. Além disso, agentes também podem trocar experiências se comunicando,
fornecer alto grau de escalabilidade, através da inclusão de novos agentes quando
necessário, e ainda fazer com que agentes assumam as atividades de outros agentes em
casos de falha. Vários modelos de agentes desenvolvidos até o momento usam o
aprendizado por reforço como algoritmo base no processo de aprendizado. Quando o
agente está inserido em ambientes pequenos ou discretos, os resultados obtidos com o uso
de métodos como Q-learning são satisfatórios. No entanto, quando o ambiente é grande
ou contínuo, o uso de métodos de aprendizado por reforço torna-se inviável, devido à
grande dimensão do espaço de estados. Nos SMA, este problema é consideravelmente
maior, já que a memória necessária passa a crescer exponencialmente com a quantidade
de agentes envolvidos na aplicação. Esta tese teve como finalidade o desenvolvimento de
um novo modelo de aprendizado autônomo para Sistemas Multi-Agentes (SMA) visando
superar estas limitações. O trabalho foi realizado em três etapas principais: levantamento
bibliográfico, seleção e implementação do modelo proposto, e desenvolvimento de estudo
de casos. O levantamento bibliográfico contemplou o estudo de agentes inteligentes e
Sistemas Multi-Agentes, buscando identificar as propriedades e limitações dos algoritmos
já desenvolvidos, as aplicações existentes, e as características desejadas em um SMA. A
seleção e utilização de um modelo neuro-fuzzy hierárquico da família RL-NFH foi
motivada especialmente pela importância de se estender a autonomia e aprendizado de
agentes através do quesito inteligência, e pela sua capacidade de superar limitações
presentes em algoritmos de aprendizado por reforço tradicionais. Inicialmente, ao modelo
anterior foram adicionados os conceitos de satisfatoriedade e não-dominação, com a
finalidade de acelerar o processo de aprendizado do algoritmo. Em seguida, o novo
modelo multi-agente foi criado, viabilizando o desenvolvimento de aplicações de
natureza tanto cooperativa como competitiva, com múltiplos agentes. Os estudos de caso
contemplaram situações distintas de cooperação e competição entre agentes autônomos.
Foram implementadas três aplicações distintas: uma aplicação benckmark do jogo da
presa-predador (Pursuit-Game); um leilão energia elétrica, em que os fornecedores de
energia fazem ofertas para atender à previsão de demanda em um período de tempo
determinado; e uma aplicação na área de gerenciamento de projetos, onde agentes
inteligentes são criados com o objetivo de fornecer estimativas de duração de atividades e
automatizar alguns processos realizados pelo Gerente de Projetos. Em todos os Estudos
de Caso, os resultados foram comparados com técnicas convencionais e/ou com o
desempenho de outros Sistemas Multi-Agente. Os resultados alcançados pelo novo
modelo se mostraram promissores. Os testes evidenciaram que o modelo teve a
capacidade de coordenar as ações entre agentes totalmente autônomos em diferentes
situações e ambientes. Além disso, o novo modelo mostrou-se genérico e flexível,
podendo ser usado no futuro em outras aplicações envolvendo múltiplos agentes. / [en] There are several benefits provided by Multi-Agent Systems (MAS). Through
parallel computing, agents can work together to better explore the decentralized structure
of a given task and speed up its completion. In addition, agents can also exchange
knowledge through communication, provide scalability by adding new agents when
appropriate, and replace troubled agents in cases of failures. A great number of existing
agent models is based on reinforcement learning algorithms for learning. When the agent
works in small or discrete environments, the results obtained with methods such as Qlearning
are satisfactory. However, when the environment is large or continuous
reinforcement learning methods become unfeasible due to the large state space. In MAS,
this problem is considerably greater, since the required memory begins to grow
exponentially with the number of agents involved in the application. The main objective
of this thesis is to develop a new model of autonomous learning for multi-agents in order
to overcome these limitations. The study consisted of three main stages: literature review,
new model development and implementation, and case studies. Literature review
included the study of intelligent agents and Multi-Agent Systems, seeking to identify the
properties and limitations of the algorithms already developed, existing applications, and
desired features in the new MAS. The choice of a neuro-fuzzy hierarchical model of the
family RL-NFH as a basis was especially motivated by the importance of extending the
autonomy and learning of the agents through intelligence. And also, because of its
capacity to overcome some of the limitations present in traditional reinforcement learning
algorithms. Initially, the concepts of satisficing and non-domination were incorporated
into the previous model to accelerate the learning algorithm. Then, the new multi-agent
model was elaborated and implemented, enabling the development of cooperative and
competitive applications, with multiple agents. Case studies have covered different
situations of cooperation and competition between autonomous agents. Three applications
were considered: the Pursuit-Game benckmark game, an electricity auction, where energy
suppliers make offers to meet forecast demand in a given period of time, and an
application in project management area, where intelligent agents are created to provide
activity duration estimates and to automate some processes done usually by the Project
Manager. In all case studies, results were compared with conventional techniques and/or
the performance of other MAS. The results achieved by the new model are encouraging.
The tests showed that the new system has the capacity to coordinate actions between fully
autonomous agents in different situations and environments. Moreover, the new model is
strongly generic and flexible. Due to these properties, it can be used in future in several
other applications involving multiple agents.
|
33 |
MECANISMO DE AUXÍLIO AO PROFESSOR PARA O AMBIENTE NETCLASS DE ENSINO-APRENDIZAGEM. / Aid mechanism to the teacher for the environment teaching-learning netclass.BRANDÃO NETO, PEDRO 18 June 2007 (has links)
Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-08-22T12:35:16Z
No. of bitstreams: 1
Pedro Brandão Neto.pdf: 938638 bytes, checksum: 79ae100045510d32510d4a705eee798f (MD5) / Made available in DSpace on 2017-08-22T12:35:16Z (GMT). No. of bitstreams: 1
Pedro Brandão Neto.pdf: 938638 bytes, checksum: 79ae100045510d32510d4a705eee798f (MD5)
Previous issue date: 2007-06-18 / FAPEMA / Distance Education environments, nowadays, provide means for learner
evaluation. This task, still, requires effort of the teacher. So, the learner
accompaniment in distance teaching-learning environments, obtained through
the observation of the learner activities, it presents referring problems to the
lack of mechanisms that offer an effective support to the teacher in the
orientation for the learner. These existent mechanisms only present results to
the teacher without to do any inference, like this, self teacher needs to analyze
those results manually in search of subsidies to do a precise orientation,
showing the possible causes and addressing the learner for the correct road.
However, to dower a mechanism with inference capacity is necessary to
capture the knowledge of as the teacher fulfills the leaner accompaniment for,
later, to transfer this knowledge for a knowledge base with inference capacity.
The necessary knowledge for the teacher to realize the learner accompaniment
in a teaching-learning environment is the evaluation criterions that are defined
by course pedagogic coordination or teacher of the teaching institution.
However, those evaluation criterions are generic and not standardized
hindering, therefore, the consent of the same; was realized a theoretical rising
for the definition of the evaluation criterions in the NetClass environment. This
research generated the knowledge on the development of a model
denominated Learner Accompaniment Support Mechanism based on
techniques of Artificial Intelligence, which is applied to the NetClass
environment of teaching-learning. / Os ambientes de Educação a Distância, atualmente, proporcionam meios de
avaliação de o aprendiz. Essa tarefa, de fato, ainda, exige muito esforço e
empenho da parte do professor. Portanto, o acompanhamento do aprendiz em
ambientes de ensino-aprendizagem a distância, obtida através da observação
das atividades dos aprendizes, apresenta problemas referentes à carência de
mecanismos que ofereçam um suporte eficaz ao professor na orientação para
o aprendiz. Esses mecanismos existentes somente apresentam resultados ao
professor sem fazer qualquer inferência, desta maneira, o próprio professor
precisa analisar manualmente esses resultados em busca de subsídios para
fazer uma orientação precisa, mostrando as possíveis causas e direcionando o
aprendiz para o caminho correto. Entretanto, para dotar um mecanismo com
capacidade de inferência faz-se necessário capturar o conhecimento de como
o professor realiza o acompanhamento do aprendiz para, posteriormente,
transferir este conhecimento para uma base de conhecimento com capacidade
de inferência. O conhecimento necessário para o professor realizar o
acompanhamento do aprendiz em um ambiente de ensino-aprendizagem são
os critérios de avaliação que são definidos pela coordenação pedagógica ou
professor do curso da instituição de ensino. No entanto, esses critérios de
avaliação são genéricos e não padronizadas dificultando, portanto, o consenso
do mesmo; foi realizado um levantamento teórico para a definição dos critérios
de avaliação no ambiente NetClass. Esta pesquisa gerou o conhecimento
sobre o desenvolvimento de um modelo de um mecanismo denominado de
Mecanismo de Apoio no Acompanhamento do Aprendiz (MAAA) o qual é
aplicado ao ambiente NetClass de ensino-aprendizagem.
|
34 |
多樣需求與資源環境中垃圾桶模式之e化服務決策研究 / Manifold Needs and Resources:Garbage Can Model of e-Service Perspective呂知穎, Lu, Chih-Ying Unknown Date (has links)
為因應人類生理或心理上的需求,而產生了形形色色之服務。隨著高科技不斷地發展,人類的未來生活,將會是充滿e化服務的生活環境。在此環境中,並非所有人均能了解各應用服務,更不知該選擇何服務才能滿足自身之多重需求。本研究擬設計一決策機制,當人們有多重需求時,能考慮有形及無形資源之有效利用,並考量不同個體之使用偏好及興趣,提供適合個人的e化服務建議。本研究之應用環境,符合垃圾桶模式中的無政府狀態之三大特性,然而原垃圾桶決策方式卻不適用於個人。因此,本研究之主體,為一智慧代理人,將以垃圾桶模式的決策原理做為基礎,並對其加以修改,分為二階段的決策過程。在第一階段,將使用一考量資源使用效率之task-chosen演算法,並搭配增強式學習中之AH-learning演算法;在第二階段,則是使用BDI代理人的架構。本研究所提出之提供e化服務建議的決策機制,預期將促使應用服務能不斷地創新及進步,並使資源獲得更有效之利用,使得人類擁有高品質的生活環境。 / There are manifold services, in order to fulfill people’s physical and mental needs. Through the continuous development of high technique, people will live in the environment surrounding e-services in the future. In this environment, it is hart for everyone to understand all e-services and choose a service to fulfill selves multiple needs. Therefore, the paper presents a decision mechanism which providing suitable e-service suggestion for everyone when they have multiple needs, considering the using utility of resources include tangible and intangible, and different preferences and interests for different people. This paper’s applying environment satisfies the three general properties of organized anarchies of “Garbage Can Model”. However, the decision method in garbage can model is not suitable to individual. The most important part of the paper is an intelligent agent, based on garbage can model theory but modify it appropriately. This intelligent agent uses two phase decision process. First phase, use a task-chosen algorism considering resource utility and AH-learning in reinforcement learning. Second phase, use the architecture of BDI agent. This paper presents a decision strategy providing e-service suggestion, and expects to promote innovative application services and use resource effectively. Finally, all people will enjoy high quality life.
|
35 |
語意式構思學習模式於協同式腦力激盪決策 / Semantic Ideation Learning for Collective Brainstorming陳延全, Chen,Yen-Chuan Unknown Date (has links)
「知識經濟」時代下,知識汰舊換新速度極快,單打獨鬥不及於團隊合作的成效,因此,不論組織或個人均須講求團隊合作。腦力激盪法(Brainstorming)即是透過團隊合作、協同決策的方式產生具有創意的解決方案。本研究結合智慧型代理人的技術與人類獨特的腦力激盪思考方式,利用智慧型代理人的自主性、溝通能力、適應力與學習能力等特性,讓智慧型代理人能在適當的時候代替腦力激盪會議的與會者出席會議,達成會議目標。為了讓智慧型代理人也能模仿人類進行創意思考,本研究以人類主要用來產生創意構思的三種聯想能力做為代理人之推論機制,並結合增強式學習的概念,設計出能根據以本體論表達之概念(Ontology-Based Concept)進行構思激盪之語意式構思學習代理人( Semantic Ideation Learning Agent,SILA ),並架構一個能讓多個SILA進行知識分享與學習的系統環境-腦力激盪式協同決策系統(Collective Brainstorming Decision System, CBDS)。本研究以傳統的腦力激盪決策模式為基礎,結合現代之網路語意表達與代理人技術,期望讓在網路上代表不同角色、身份的代理人,基於其所擁有之構思知識庫 (Idea Knowledge Base),透過代理人之間的溝通與知識分享,達成代理人自動化協同決策(Collective Decision)之目標。 / In Knowledge Economy Era, the organization and individual are emphasizing on the teamwork instead of single play because of better effectiveness. Brainstorming is a solution that can help organization to generate creative ideas through teamwork and collaboration. This research combines human’s unique brainstorming thinking and the intelligent agent technique for devising an automated decision agent called Semantic Ideation Learning Agent (SILA) (that can represent a session participant to engage the action of brainstorming). In order to make a SILA thinking like human, our research presents a method of Reinforcement Learning grounded on three capabilities of human’s association (similarity, contiguity, contrast) as the SILA’s inference mechanism. Furthermore, the Collective Brainstorming Decision System was build to provide an environment where SILAs can learn and share their knowledge. The aim of this research is to reach automatic collective decision in a brainstorming session through the collaboration of the agents based on the brainstorming decision model and some modern information techniques including knowledge base, semantic web and intelligent agents.
|
36 |
A Logical Theory of Joint Ability in the Situation CalculusGhaderi, Hojjat 17 February 2011 (has links)
Logic-based formalizations of dynamical systems are central to the field of knowledge representation and reasoning. These formalizations can be used to model agents that act, reason,and perceive in a changing and incompletely known environment. A key aspect of reasoning about agents and their behaviors is the notion of joint ability. A team of agents is jointly able
to achieve a goal if despite any incomplete knowledge or even false beliefs about the world or each other, they still know enough to be able to get to a goal state, should they choose to do so. A particularly challenging issue associated with joint ability is how team members can coordinate their actions. Existing approaches often require the agents to communicate to agree
on a joint plan. In this thesis, we propose an account of joint ability that supports coordination among agents without requiring communication, and that allows for agents to have incomplete (or even false) beliefs about the world or the beliefs of other agents. We use ideas from game theory to address coordination among agents. We introduce the notion of a strategy for each
agent which is basically a plan that the agent knows how to follow. Each agent compares her strategies and iteratively discards those that she believes are not good considering the strategies that the other agents have kept. Our account is developed in the situation calculus, a logical language suitable for representing and reasoning about action and change that is extended to support reasoning about multiple agents. Through several examples involving public, private, and sensing actions, we demonstrate how symbolic proof techniques allow us to reason about team ability despite incomplete specifications about the beliefs of agents.
|
37 |
[en] METHODS FOR ACCELERATION OF LEARNING PROCESS OF REINFORCEMENT LEARNING NEURO-FUZZY HIERARCHICAL POLITREE MODEL / [pt] MÉTODOS DE ACELERAÇÃO DE APRENDIZADO APLICADO AO MODELO NEURO-FUZZY HIERÁRQUICO POLITREE COM APRENDIZADO POR REFORÇOFABIO JESSEN WERNECK DE ALMEIDA MARTINS 04 October 2010 (has links)
[pt] Neste trabalho foram desenvolvidos e avaliados métodos com o objetivo de melhorar e acelerar o processo de aprendizado do modelo de Reinforcement Learning Neuro-Fuzzy Hierárquico Politree (RL-NFHP). Este modelo pode ser utilizado para dotar um agente de inteligência através de processo de Aprendizado por Reforço (Reinforcement Learning). O modelo RL-NFHP apresenta as seguintes características: aprendizado automático da estrutura do modelo; auto-ajuste dos parâmetros associados à estrutura; capacidade de aprendizado da ação a ser adotada quando o agente está em um determinado estado do ambiente; possibilidade de lidar com um número maior de entradas do que os sistemas neuro-fuzzy tradicionais; e geração de regras linguísticas com hierarquia. Com intenção de melhorar e acelerar o processo de aprendizado do modelo foram implementadas seis políticas de seleção, sendo uma delas uma inovação deste trabalho (Q-DC-roulette); implementado o método early stopping para determinação automática do fim do treinamento; desenvolvido o eligibility trace cumulativo; criado um método de poda da estrutura, para eliminação de células desnecessárias; além da reescrita do código computacional original. O modelo RL-NFHP modificado foi avaliado em três aplicações: o benchmark Carro na Montanha simulado, conhecido na área de agentes autônomos; uma simulação robótica baseada no robô Khepera; e uma num robô real NXT. Os testes efetuados demonstram que este modelo modificado se ajustou bem a problemas de sistemas de controle e robótica, apresentando boa generalização. Comparado o modelo RL-NFHP modificado com o original, houve aceleração do aprendizado e obtenção de menores modelos treinados. / [en] In this work, methods were developed and evaluated in order to improve and accelerate the learning process of Reinforcement Learning Neuro-Fuzzy Hierarchical Politree Model (RL-NFHP). This model is employed to provide an agent with intelligence, making it autonomous, due to the capacity of ratiocinate (infer actions) and learning, acquired knowledge through interaction with the environment by Reinforcement Learning process. The RL-NFHP model has the following features: automatic learning of structure of the model; self-adjustment of parameters associated with its structure, ability to learn the action to be taken when the agent is in a particular state of the environment; ability to handle a larger number of inputs than the traditional neuro-fuzzy systems; and generation of rules with linguistic interpretable hierarchy. With the aim to improve and accelerate the learning process of the model, six selection action policies were developed, one of them an innovation of this work (Q-DC-roulette); implemented the early stopping method for automatically determining the end of the training; developed a cumulative eligibility trace; created a method of pruning the structure, for removing unnecessary cells; in addition to rewriting the original computer code. The modified RL-NFHP model was evaluated in three applications: the simulated benchmark Car-Mountain problem, well known in the area of autonomous agents; a simulated application in robotics based on the Khepera robot; and an application in a real robot. The experiments show that this modified model fits well the problems of control systems and robotics, with a good generalization. Compared the modified RL-NFHP model with the original one, there was acceleration of learning process and smaller structures of the model trained.
|
38 |
Um sistema para o reconhecimento da feição edificação em imagem digital com agentes inteligentes. / A building recognition system in digital image based on intelligent agents.Pryscila de Jesus de Sousa 10 October 2011 (has links)
O objetivo desta dissertação foi criar uma nova abordagem para identificar de maneira automática feições do tipo edificação em uma imagem digital. Tal identificação seria de interesse de órgãos públicos que lidam com planejamento urbano para fins de controle da ocupação humana irregular. A abordagem criada utilizou agentes de software especialistas para proceder com o processamento da segmentação e reconhecimento de feições na imagem digital. Os agentes foram programados para tratar uma imagem colorida com o padrão Red, Green e Blue (RGB). A criação desta nova abordagem teve como motivação o fato das atuais técnicas existentes de segmentação e classificação de imagens dependerem sobremaneira dos seus usuários. Em outras palavras, pretendeu-se com a abordagem em questão permitir que usuários menos técnicos pudessem interagir com um sistema classificador, sem a necessidade de profundos conhecimentos de processamento digital de imagem. Uma ferramenta protótipo foi desenvolvida para testar essa abordagem, que emprega de forma inusitada, agentes inteligentes, com testes feitos em recortes de ortofotos digitais do Município de Angra dos Reis (RJ). / The purpose of this dissertation has been to create a new approach in order to recognition features of buildings in a digital image in an automatic way. Such recognition features would be interesting of government agencies that deals with urban planning for irregular human occupation control. The approach created has employed specialist software agents to proceed with the segmentation processing and features recognition in the digital images. The agents have been programmed to manipulate colored images with the Red, Green and Blue pattern (RGB). The creation of this new approach has been motivated by the fact of existing segmentation techniques and classification of images greatly depend on its users. In other words, with the approach discussed it has been intended to allow less technical users to interact with a classifier system, without requiring deep knowledge of digital image processing. A prototype tool has been developed to test this approach, which employs in an unusual way, intelligent agents, with tests done in digital orthophotos of the city of Angra dos Reis (RJ).
|
39 |
Um sistema para o reconhecimento da feição edificação em imagem digital com agentes inteligentes. / A building recognition system in digital image based on intelligent agents.Pryscila de Jesus de Sousa 10 October 2011 (has links)
O objetivo desta dissertação foi criar uma nova abordagem para identificar de maneira automática feições do tipo edificação em uma imagem digital. Tal identificação seria de interesse de órgãos públicos que lidam com planejamento urbano para fins de controle da ocupação humana irregular. A abordagem criada utilizou agentes de software especialistas para proceder com o processamento da segmentação e reconhecimento de feições na imagem digital. Os agentes foram programados para tratar uma imagem colorida com o padrão Red, Green e Blue (RGB). A criação desta nova abordagem teve como motivação o fato das atuais técnicas existentes de segmentação e classificação de imagens dependerem sobremaneira dos seus usuários. Em outras palavras, pretendeu-se com a abordagem em questão permitir que usuários menos técnicos pudessem interagir com um sistema classificador, sem a necessidade de profundos conhecimentos de processamento digital de imagem. Uma ferramenta protótipo foi desenvolvida para testar essa abordagem, que emprega de forma inusitada, agentes inteligentes, com testes feitos em recortes de ortofotos digitais do Município de Angra dos Reis (RJ). / The purpose of this dissertation has been to create a new approach in order to recognition features of buildings in a digital image in an automatic way. Such recognition features would be interesting of government agencies that deals with urban planning for irregular human occupation control. The approach created has employed specialist software agents to proceed with the segmentation processing and features recognition in the digital images. The agents have been programmed to manipulate colored images with the Red, Green and Blue pattern (RGB). The creation of this new approach has been motivated by the fact of existing segmentation techniques and classification of images greatly depend on its users. In other words, with the approach discussed it has been intended to allow less technical users to interact with a classifier system, without requiring deep knowledge of digital image processing. A prototype tool has been developed to test this approach, which employs in an unusual way, intelligent agents, with tests done in digital orthophotos of the city of Angra dos Reis (RJ).
|
40 |
MODELOG : model-oriented development with executable logical object generationde Souza Ramalho, Franklin January 2007 (has links)
Made available in DSpace on 2014-06-12T15:54:14Z (GMT). No. of bitstreams: 2
arquivo6624_1.pdf: 2520643 bytes, checksum: b69b2ba2918606ef2803cf6322485425 (MD5)
license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5)
Previous issue date: 2007 / The Unified Modeling Language (UML) has far outgrown its initial purpose as a standard
visual notation for constructing intuitive, high-level blueprint models of object-oriented
software. A series of extension, such as OCL, XMI, ASL, MOF, UML profiles, and
different proposed formal semantics, to the language and to its application scope have fedoff
each other in synergy. While these extensions constitute a sound starting point to make
UML the pivotal element for the Model-Driven Engineering (MDE) and Semantic Web
(SW) visions, many other building blocks are still missing, including: (1) Complete formal
semantics for UML and OCL; (2) UML inference engine based on such semantics to
support model checking and intelligent agent reasoning with UML semantic web
ontologies; (3) UML model compilers to fully automate both structural and behavioral code
generation from detailed UML/OCL models.
In this thesis, we present the MODELOG framework based on a single proposal to
address these three issues: it consists of an automatic mapping from fully refined
UML/OCL models to the object-oriented logic programs in the Flora-2, an executable and
Turing-complete language with a well-defined formal semantics.
We developed the MODELOG mapping as model transformations. As source for
these transformations, we reused the UML2 and OCL2 metamodels provided by OMG and
as target we developed a Flora metamodel. As we experimented and compared two
language to implement the transformation: the dedicated transformation language ATL
based on OCL, and Flora itself viewed as a model transformation language. As validating
case study for MODELOG, we developed the Triangram robotic assembly puzzle. It is an
example of planning tasks often executed by agents and it requires a complex class
hierarchy together with complex structural and behavioral constraints. The Flora PSM of
this puzzle was entirely automatically generated from its UML/OCL model by applying the
MODELOG transformations.
The main originality of this thesis is twofold. First, it simultaneously tackles various
gaps in UML for its new extended role: formal specification language, MDE, semantic web
services and agent engineering, Second, it does so without putting forward any new
language but only by reusing a theoretically consolidated and efficiently implemented one.
In contrast, previous proposals tend be limited to one or two such applications while often
proposing new languages.
The thesis makes contributions to various fields:
For MDE, it shows the feasibility of completely automated full structural and
behavioral executable code generation from a UML/OCL PIM, on a non-trivial,
complex case study involving automated reasoning;
For agile development, it reconcile that the robustness of formal methods with
the early testing of fast prototyping, since MODELOG not only generates
executable code, but also model checking code from UML/OCL models.
For UML and OCL, it provides a fully integrated denotational formal semantics
in Transaction Frame Logic on which Flora is based;
For logic programming, it provides compositional metamodels of all the
languages integrated in the Flora platform that clarify their relations and make them
available as source or target language for a variety of MDE tasks such as model
checking and fast prototyping; it also provides a way to develop logic programs
using the consolidated full-life cycle object-oriented software engineering
processes;
For agent engineering, it shows how to get combine the complementary
strengths of the object-oriented and logic paradigms, the two mostly widely reused
in agent-oriented methods;
For the semantic web service development, its introduced a highly automated
MDE approach allowing to model them visually with UML-based industrial
strengths method and CASE tools, and automatically generate their specification in the
W3C standard SWSL, which is a XML-enabled variant of Flora
|
Page generated in 0.0525 seconds