Para alunos de cursos superiores de Sistemas de Informação e afins, aprender a programar computadores é fundamental. No entanto, ensinar programação por meio de métodos tradicionais tem se tornado cada vez mais desafiante devido a fatores recentes, tais como transformações na maneira de aprender das novas gerações e o surgimento de novas máquinas computacionais. Neste contexto, a aprendizagem baseada em projeto possui potencial para beneficiar a educação em programação. Há especial relevância para a abordagem Ágil de projetos, por possuir destaque no mercado de trabalho e origem atrelada ao próprio desenvolvimento de softwares. Por outro lado, foram encontrados poucos estudos de relevância internacional relatando a utilização da aprendizagem baseada em projeto ágil (APjBL) no contexto do ensino superior brasileiro. O objetivo geral desta pesquisa é analisar os benefícios de um método APjBL para os alunos de programação no ensino superior brasileiro quando comparado ao método tradicional de ensino, principalmente com relação a quatro benefícios de interesse desta pesquisa: o rendimento escolar, a motivação para aprender, a comunicação verbal e a exploração vocacional dos alunos. Nesta pesquisa empírica e de natureza aplicada, foi feita a opção pelo quase-experimento em um curso superior tecnológico de Análise e Desenvolvimento de Sistemas durante o primeiro semestre de 2018. A análise foi quali-quantitativa e os critérios de comparação foram os quatro benefícios mencionados. Como intervenção educacional APjBL, foi escolhido o método Agile Model for Projects in Computing Education (AMoPCE) e adaptado ao contexto. Participaram desta pesquisa 151 alunos e cinco professores, divididos em grupos de experimento e controle. Ao final do semestre, os alunos responderam a um questionário eletrônico sobre três benefícios: motivação para aprender, comunicação verbal e exploração vocacional. Foram coletadas as notas e as frequências dos alunos, além das percepções dos professores por meio de entrevistas individuais semiestruturadas. Dados históricos das disciplinas foram coletados para ajudar a explicar os fenômenos. Conclui-se que, no contexto geral da pesquisa, para a disciplina de conteúdo predominantemente procedural (prático), AMoPCE beneficiou a motivação extrínseca, a frequência escolar e a escuta ativa na comunicação. Para a disciplina altamente procedural, AMoPCE apresentou mais dois benefícios: motivação para aprender intrínseca e exploração vocacional intrínseca. Em suma, foi verificada uma hipótese de pesquisa, AMoPCE beneficia a motivação para aprender, na disciplina altamente procedural / To learn computer programming is an essential topic to undergraduate students at Information System and related higher education courses. However, teaching programming using traditional methods have become much more challenging due to recent reasons, like changes in the manner that the new generation is now prone to learn, and the arising of new programmable devices. The Project-Based Learn may offer potential benefits to the computer programming education, especially the Agile approach, given its origin at the software development process. And yet there were found a few relevant international studies regarding the Agile Project-Based Learn (APjBL) to cope with the computer programming education at Brazilian Information System higher education. The research leading objective was to analyze the main benefits of the APjBL teaching method when compared to the traditional teaching method, mainly the following four students benefits: academic performance, motivation to learn, verbal communication and vocational inquiry aspects. As the comparison criterion, we selected these four benefits through specific instruments adapted to this research. We also chose the Agile Model for Projects in Computing Education (AMoPCE) as the APjBL teaching method, which was originated from previous researches and that we adapted to this context. This research adopted an experimental approach via a quasi-experiment at the System Development and Analysis higher education course, during the first semester of 2018. A total of 151 students and five teachers participated in this research. By the end of the experiment, the students replied to an electronic questionnaire regarding three benefits: motivation to learn, verbal communication and vocational inquiry aspects. Five professors participated in an individual interview. Students grades and frequency and their historical data were also collected. As results, we found that for procedural (practical) content, AMoPCE presented at least the following benefits: extrinsic motivation, class frequency, and verbal communication via active listening. Even further, when the class is highly procedural, AMoPCE presents two benefits more: intrinsic motivation to learn and intrinsic vocational inquiry. In summary, the hypothesis that AMoPCE benefits the motivation was confirmed at highly procedural classes

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING / Bibliography: leaves 90-91. / by Brij Mohan Masand. / M.S.

Agent-based simulations are an effective simulation technique that can flexibly be applied to real-world business problems. By integrating such simulations into business games, they become a widely accepted educational instrument in the context of business training. Not only can they be used to train standard behaviour in training scenarios but they can also be used for open experimentation to discover structure in complex contexts (e.g. complex adaptive systems) and to verify behaviours that have been predicted on the basis of theoretical considerations. Traditional modelling techniques are built on mathematical models consisting of differential or difference equations (e.g. the well-known system dynamics approach). However, individual behaviour is not visible in these equations. This problem is addressed by using software agents to simulate individuals and to model their actions in response to external stimuli. To be effective, business training tools have to provide sufficiently realistic models of real-world aspects. Ideally, system effects on a macroscopic level are caused by behaviour of system components on a more microscopic level. For instance, in modelling market mechanisms market participants can explicitly be modelled as agents with individual behaviour and personal goals. Agents can communicate and act on the basis of what they know and which communication acts they perform. The evolution of the market then depends on the actions of the participants directly and not on abstract mathematical expressions. Generally, agent-based modelling is a challenging task, when modelling knowledge and behaviour. With the rise of the so-called semantic web ontologies have become popular, allowing the representation of knowledge using standardised formal languages which can be made available to agents acting in a simulation. However, the combination of agent-based systems with ontologies has not yet been researched sufficiently, because both concepts (web ontology languages and agent oriented programming languages) have been developed independently and the link has not yet been built adequately. Using ontologies as a knowledge base allows access to powerful standardised inference engines that offer leverage for the decision process of the agent. Agents can then determine their actions in accordance with this knowledge. To model agents using ontologies creates a new perspective for multi-agent simulation scenarios as programming details are reduced and a separation of modelling aspects from coding details is promising as business simulation scenarios can be set up with a reduced development effort. This thesis focuses on how ontologies can be integrated utilising the agent framework Jadex. A basic architecture with layered ontologies and its integration into the belief-desire-intention (BDI) agent model is presented. The abstract level of the approach guarantees applicability to different simulation scenarios which can be modelled by creating appropriate ontologies. Examples are based upon the simulation of market mechanisms within the context of different industries. The approach is implemented in the integrated simulation environment AGADE which incorporates agent-based and semantic technologies. Simulations for different scenarios that model typical market scenarios are presented.

The rapid growth of e-commerce has many associated security concerns. Thus, several studies to develop secure online authentication systems have emerged. Most studies begin with the premise that the intermediate network is the primary point of compromise. In this thesis, we assume that the point of compromise lies within the end-host or browser; this security threat is called the man-in-the-browser (MITB) attack. MITB attacks can bypass security measures of public key infrastructures (PKI), as well as encryption mechanisms for secure socket layers and transport layer security (SSL/TLS) protocol. This thesis focuses on developing a system that can circumvent MITB attacks using a two-phase secure-user authentication system, with phases that include challenge and response generation. The proposed system represents the first step in conducting an online business transaction. The proposed authentication system design contributes to protect the confidentiality of the initiating client by requesting minimal and non-confidential information to bypass the MITB attack and transition the authentication mechanism from the infected browser to a mobile-based system via a challenge/response mechanism. The challenge and response generation process depends on validating the submitted information and ensuring the mobile phone legitimacy. Both phases within the MUAS context mitigate the denial-of-service (DOS) attack via registration information, which includes the client's mobile number and the International Mobile Equipment Identity (IMEI) of the client's mobile phone. This novel authentication scheme circumvents the MITB attack by utilising the legitimate client's personal mobile phone as a detached platform to generate the challenge response and conduct business transactions. Although the MITB attacker may have taken over the challenge generation phase by failing to satisfy the required security properties, the response generation phase generates a secure response from the registered legitimate mobile phone by employing security attributes from both phases. Thus, the detached challenge- and response generation phases are logically linked.

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING / Bibliography: leaf 22. / by Inwhan Choi. / B.S.

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Richard Michael Heller. / B.S.

Para alunos de cursos superiores de Sistemas de Informação e afins, aprender a programar computadores é fundamental. No entanto, ensinar programação por meio de métodos tradicionais tem se tornado cada vez mais desafiante devido a fatores recentes, tais como transformações na maneira de aprender das novas gerações e o surgimento de novas máquinas computacionais. Neste contexto, a aprendizagem baseada em projeto possui potencial para beneficiar a educação em programação. Há especial relevância para a abordagem Ágil de projetos, por possuir destaque no mercado de trabalho e origem atrelada ao próprio desenvolvimento de softwares. Por outro lado, foram encontrados poucos estudos de relevância internacional relatando a utilização da aprendizagem baseada em projeto ágil (APjBL) no contexto do ensino superior brasileiro. O objetivo geral desta pesquisa é analisar os benefícios de um método APjBL para os alunos de programação no ensino superior brasileiro quando comparado ao método tradicional de ensino, principalmente com relação a quatro benefícios de interesse desta pesquisa: o rendimento escolar, a motivação para aprender, a comunicação verbal e a exploração vocacional dos alunos. Nesta pesquisa empírica e de natureza aplicada, foi feita a opção pelo quase-experimento em um curso superior tecnológico de Análise e Desenvolvimento de Sistemas durante o primeiro semestre de 2018. A análise foi quali-quantitativa e os critérios de comparação foram os quatro benefícios mencionados. Como intervenção educacional APjBL, foi escolhido o método Agile Model for Projects in Computing Education (AMoPCE) e adaptado ao contexto. Participaram desta pesquisa 151 alunos e cinco professores, divididos em grupos de experimento e controle. Ao final do semestre, os alunos responderam a um questionário eletrônico sobre três benefícios: motivação para aprender, comunicação verbal e exploração vocacional. Foram coletadas as notas e as frequências dos alunos, além das percepções dos professores por meio de entrevistas individuais semiestruturadas. Dados históricos das disciplinas foram coletados para ajudar a explicar os fenômenos. Conclui-se que, no contexto geral da pesquisa, para a disciplina de conteúdo predominantemente procedural (prático), AMoPCE beneficiou a motivação extrínseca, a frequência escolar e a escuta ativa na comunicação. Para a disciplina altamente procedural, AMoPCE apresentou mais dois benefícios: motivação para aprender intrínseca e exploração vocacional intrínseca. Em suma, foi verificada uma hipótese de pesquisa, AMoPCE beneficia a motivação para aprender, na disciplina altamente procedural / To learn computer programming is an essential topic to undergraduate students at Information System and related higher education courses. However, teaching programming using traditional methods have become much more challenging due to recent reasons, like changes in the manner that the new generation is now prone to learn, and the arising of new programmable devices. The Project-Based Learn may offer potential benefits to the computer programming education, especially the Agile approach, given its origin at the software development process. And yet there were found a few relevant international studies regarding the Agile Project-Based Learn (APjBL) to cope with the computer programming education at Brazilian Information System higher education. The research leading objective was to analyze the main benefits of the APjBL teaching method when compared to the traditional teaching method, mainly the following four students benefits: academic performance, motivation to learn, verbal communication and vocational inquiry aspects. As the comparison criterion, we selected these four benefits through specific instruments adapted to this research. We also chose the Agile Model for Projects in Computing Education (AMoPCE) as the APjBL teaching method, which was originated from previous researches and that we adapted to this context. This research adopted an experimental approach via a quasi-experiment at the System Development and Analysis higher education course, during the first semester of 2018. A total of 151 students and five teachers participated in this research. By the end of the experiment, the students replied to an electronic questionnaire regarding three benefits: motivation to learn, verbal communication and vocational inquiry aspects. Five professors participated in an individual interview. Students grades and frequency and their historical data were also collected. As results, we found that for procedural (practical) content, AMoPCE presented at least the following benefits: extrinsic motivation, class frequency, and verbal communication via active listening. Even further, when the class is highly procedural, AMoPCE presents two benefits more: intrinsic motivation to learn and intrinsic vocational inquiry. In summary, the hypothesis that AMoPCE benefits the motivation was confirmed at highly procedural classes

Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial digital games. AI for non playing characters (NPC) in computer games tends to come from people with computing skills well beyond the average user. The prime reason behind the lack of involvement of novice users in creating AI behaviors for NPC's in computer games is that construction of high quality AI behaviors is a hard problem. There are two reasons for it. First, creating a set of AI behavior requires specialized skills in design and programming. The nature of the process restricts it to certain individuals who have a certain expertise in this area. There is little understanding of how the behavior authoring process can be simplified with easy-to-use authoring environments so that novice users (without programming and design experience) can carry out the behavior authoring task. Second, the constructed AI behaviors have problems and bugs in them which cause a break in player expe- rience when the problematic behaviors repeatedly fail. It is harder for novice users to identify, modify and correct problems with the authored behavior sets as they do not have the necessary debugging and design experience. The two issues give rise to a couple of interesting questions that need to be investigated: a) How can the AI behavior construction process be simplified so that a novice user (without program- ming and design experience) can easily conduct the authoring activity and b) How can the novice users be supported to help them identify and correct problems with the authored behavior sets? In this thesis, I explore the issues related to the problems highlighted and propose a solution to them within an application domain, named Second Mind(SM). In SM novice users who do not have expertise in computer programming employ an authoring interface to design behaviors for intelligent virtual characters performing a service in a virtual world. These services range from shopkeepers to museum hosts. The constructed behaviors are further repaired using an AI based approach. To evaluate the construction and repair approach, we conduct experiments with human subjects. Based on developing and evaluating the solution, I claim that a design solution with behavior timeline based interaction design approach for behavior construction supported by an understandable vocabulary and reduced feature representation for- malism enables novice users to author AI behaviors in an easy and understandable manner for NPCs performing a service in a virtual world. I further claim that an introspective reasoning approach based on comparison of successful and unsuccessful execution traces can be used as a means to successfully identify breaks in player ex- perience and modify the failures to improve the experience of the player interacting with NPCs performing a service in a virtual world. The work contributes in the following three ways by providing: 1) a novel introspective reasoning approach for successfully detecting and repairing failures in AI behaviors for NPCs performing a service in a virtual world.; 2) a novice user understandable authoring environment to help them create AI behaviors for NPCs performing a service in a virtual world in an easy and understandable manner; and 3) Design, debugging and testing scaffolding to help novice users modify their authored AI behaviors and achieve higher quality modified AI behaviors compared to their original unmodified behaviors.

Nature has created complex biological structures that exhibit intelligent behaviour through an evolutionary process. Thus, intelligence and evolution are intimately connected. This has inspired evolutionary computation (EC) that simulates the evolutionary process to develop powerful techniques such as genetic algorithms (GAs), genetic programming (GP), evolutionary strategies (ES) and evolutionary programming (EP) to solve real-world problems in learning, control, optimization and classification. GP discovers the relationship among data and expresses it as a LISP-S expression i.e., a computer program. Thus the goal of program discovery as a solution for a problem is addressed by GP in the framework of evolutionary computation. In this thesis, we address for the first time the problem of applying GP to mu1ticategory pattern classification. In supervised pattern classification, an input vector of m dimensions is mapped onto one of the n classes. It has a number of application areas such as remote sensing, medical diagnosis etc., A supervised classifier is developed by using a training set that contains representative samples of various classes present in the application. Supervised classification has been done earlier with maximum likelihood classifier: neural networks and fuzzy logic. The major considerations in applying GP to pattern classification are listed below: (i) GP-based techniques are data distribution-free i.e., no a priori knowledge is needed abut the statistical distribution of the data or no assumption such as normal distribution for data needs to be made as in MLC. (ii) GP can directly operate on the data in its original form. (iii) GP can detect the underlying but unknown relationship that mists among data and express it as a mathematical LISP S-expression. The generated LISP S-expressions can be directly used in the application environment. (iv) GP can either discover the most important discriminating features of a class during evolution or it requires minor post-processing of the LISP-S expression to discover the discriminant features. In a neural network, the knowledge learned by the neural network about the data distributions is embedded in the interconnection weights and it requires considerable amount of post-processing of the weights to understand the decision of the neural network. In 2-category pattern classification, a single GP expression is evolved as a discriminant function. The output of the GP expression can be +l for samples of one class and -1 for samples of the other class. When the GP paradigm is applied to an n-class problem, the following questions arise: Ql. As a typical GP expression returns a value (+l or -1) for a 2-class problem, how does one apply GP for the n-class pattern classification problem? Q2. What should be the fitness function during evolution of the GP expressions? Q3. How does the choice of a function set affect the performance of GP-based classification? Q4. How should training sets be created for evaluating fitness during the evolution of GP classifier expressions? Q5. How does one improve learning of the underlying data distributions in a GP framework? Q6. How should conflict resolution be handled before assigning a class to the input feature vector? Q7. How does GP compare with other classifiers for an n-class pattern classification problem? The research described here seeks to answer these questions. We show that GP can be applied to an n-category pattern classification problem by considering it as n 2-class problems. The suitability of this approach is demonstrated by considering a real-world problem based on remotely sensed satellite images and Fisher's Iris data set. In a 2-class problem, simple thresholding is sufficient for a discriminant function to divide the feature space into two regions. This means that one genetic programming classifier expression (GPCE) is sufficient to say whether or not the given input feature vector belongs to that class; i.e., the GP expression returns a value (+1 or -1). As the n-class problem is formulated as n 2-class problems, n GPCEs are evolved. Hence, n GPCE specific training sets are needed to evolve these n GPCEs. For the sake of illustration, consider a 5-class pat tern classification problem. Let n, be the number of samples that belong to class j, and N, be the number of samples that do not belong to class j, (j = 1,..., 5). Thus, N1=n2+n3+n4+n5 N2=n1+n3+n4+n5 N3=n1+n2+n4+n5 N4=n1+n2+n3+n5 N5=n1+n2+n3+n4 Thus, When the five class problem is formulated as five 2-class problems. we need five GPCEs as discriminant functions to resolve between n1 and N1, n2 and N2, n3 and N3, n4 and N4 and lastly n5 and N5. Each of these five 2-class problems is handled as a separate 2-class problem with simple thresholding. Thus, GPCE# l resolves between samples of class# l and the remaining n - 1 classes. A training set is needed to evaluate the fitness of GPCE during its evolution. If we directly create the training set, it leads to skewness (as n1 < N1). To overcome the skewness, an interleaved data format is proposed for the training set of a GPCE. For example, in the training set of GPCE# l, samples of class# l are placed alternately between samples of the remaining n - 1 classes. Thus, the interleaved data format is an artifact to create a balanced training set. Conventionally, all the samples of a training set are fed to evaluate the fitness of every member of the population in each generation. We call this "global" learning 3s GP tries to learn the entire training set at every stage of the evolution. We have introduced incremental learning to simplify the task of learning for the GP paradigm. A subset of the training set is fed and the size of the subset is gradually increased over time to cover the entire training data. The basic motivation for incremental learning is to improve learning during evolution as it is easier to learn a smaller task and then to progress from a smaller task to a bigger task. Experimental results are presented to show that the interleaved data format and incremental learning improve the performance of the GP classifier. We also show that the GPCEs evolved with an arithmetic function set are able to track variation in the input better than GPCEs evolved with function sets containing logical and nonlinear elements. Hence, we have used arithmetic function set, incremental learning, and interleaved data format to evolve GPCEs in our simulations. AS each GPCE is trained to recognize samples belonging to its own class and reject samples belonging to other classes a strength of association measure is associated with each GPCE to indicate the degree to which it can recognize samples belonging to its own class. The strength of association measures are used for assigning a class to an input feature vector. To reduce misclassification of samples, we also show how heuristic rules can be generated in the GP framework unlike in either MLC or the neural network classifier. We have also studied the scalability and generalizing ability of the GP classifier by varying the number of classes. We also analyse the performance of the GP classifier by considering the well-known Iris data set. We compare the performance of classification rules generated from the GP classifier with those generated from neural network classifier, (24.5 method and fuzzy classifier for the Iris data set. We show that the performance of GP is comparable to other classifiers for the Iris data set. We notice that the classification rules can be generated with very little post-processing and they are very similar to the rules generated from the neural network and C4.5 for the Iris data set. Incremental learning influences the number of generations available for GP to learn the data distribution of classes whose d is -1 in the interleaved data format. This is because the samples belonging to the true class (desired output d is +1) are alternately placed between samples belonging to other classes i.e., they are repeated to balance the training set in the interleaved data format. For example, in the evolution of GPCE for class# l, the fitness function can be fed initially with samples of class#:! and subsequently with the samples of class#3, class#4 and class#. So in the evaluation of the fitness function, the samples of class#kt5 will not be present when the samples of class#2 are present in the initial stages. However, in the later stages of evolution, when samples of class#5 are fed, the fitness function will utilize the samples of both class#2 and class#5. As learning in evolutionary computation is guided by the evaluation of the fitness function, GPCE# l gets lesser number of generations to learn how to reject data of class#5 as compared to the data of class#2. This is because the termination criterion (i.e., the maximum number of generations) is defined a priori. It is clear that there are (n-l)! Ways of ordering the samples of classes whose d is -1 in the interleaved data format. Hence a heuristic is presented to determine a possible order to feed data of different classes for the GPCEs evolved with incremental learning and interleaved data format. The heuristic computes an overlap index for each class based on its spatial spread and distribution of data in the region of overlap with respect to other classes in each feature. The heuristic determines the order in which classes whose desired output d is –1 should be placed in each GPCE-specific training set for the interleaved data format. This ensures that GP gets more number of generations to learn about the data distribution of a class with higher overlap index than a class with lower overlap index. The ability of the GP classifier to learn the data distributions depends upon the number of classes and the spatial spread of data. As the number of classes increases, the GP classifier finds it difficult to resolve between classes. So there is a need to partition the feature space and identify subspaces with reduced number of classes. The basic objective is to divide the feature space into subspaces and hence the data set that contains representative samples of n classes into subdata sets corresponding to the subspaces of the feature space, so that some of the subdata sets/spaces can have data belonging to only p classes (p < n). The GP classifier is then evolved independently for the subdata sets/spaces of the feature space. This results in localized learning as the GP classifier has to learn the data distribution in only a subspace of the feature space rather than in the entire feature space. By integrating the GP classifier with feature space partitioning (FSP), we improve classification accuracy due to localized learning. Although serial computers have increased steadily in their performance, the quest for parallel implementation of a given task has continued to be of interest in any computationally intensive task since parallel implementation leads to a faster execution than a serial implementation As fitness evaluation, selection strategy and population structures are used to evolve a solution in GP, there is scope for a parallel implementation of GP classifier. We have studied distributed GP and massively parallel GP for our approach to GP-based multicategory pattern classification. We present experimental results for distributed GP with Message Passing Interface on IBM SP2 to highlight the speedup that can be achieved over the serial implementation of GP. We also show how data parallelism can be used to further speed up fitness evaluation and hence the execution of the GP paradigm for multicategory pat tern classification. We conclude that GP can be applied to n-category pattern classification and its potential lies in its simplicity and scope for parallel implementation. The GP classifier developed in this thesis can be looked upon as an addition to the earlier statistical, neural and fuzzy approaches to multicategory pattern classification.

In the future, computers will be virtually everywhere: carried by everyone and integrated into the environment. The increased computation and communication capabilities will enable intelligent environments that react to occupants through automated decision-making. Devices (sensors and actuators) are the key to making intelligent environments a reality. We believe that devices must be made more approachable for average users. Existing approaches to application development for intelligent environments require detailed knowledge about devices and their low-leveling programming interfaces, which greatly limits the number of potential users. Instead of limiting users, we must enable everyone to program the devices around them. Intelligent environments will not be commonplace until average people can set up and manage the hardware and software necessary for their personalized applications. In simplifying the programming of intelligent environments, we first made sensors and actuators accessible to average programmers then extended our work to end-users. We term the former contribution Sensor Enablement for Average Programmers (SEAP); the latter work is Sensor Enablement for End-Users (SEEU). In our experience, devices’ disparate, niche programming languages and communication protocols presented great difficulty in developing intelligent environments. To ease the development effort for average programmers, we abstracted and standardized complex sensor and actuator interactions, allowing users to instead think in terms of well-understood web applications. Users have said that SEAP is easy-to-use and exciting. But what about average people, end-users? We found that end-users are incredibly interested in intelligent environments. By engaging end-users we can create intelligent environments even faster and allow domain experts to tailor their environment. This dissertation’s second contribution, Sensor Enablement for End-Users (SEEU) provides a visual programming interface that allows users to create personalized automated behaviors given available devices and data. We performed several user studies to uncover people’s desires for intelligent environments and determine the best interface for managing an intelligent environment. SEEU combines an intuitive interface with the power and flexibility of SEAP. SEEU is a usable end-user programming framework that allows average people to create useful applications for their intelligent environments. With SEEU and SEAP, we simplified the development of intelligent environments, reducing barriers to adoption of emerging sensing and actuation technologies. We demonstrated the feasability with a series of user studies. / text