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Reinforcement learning is a method in which agent/agents obtain a positive or negative reward to do an efficient operation. In this way, the performance will be very suitable for the systems which are naturally complicated for deriving the differential equations. This can be a good alternative to other control areas. One of the main disadvantages of this method is considering the discrete actions during it. However, many of dynamical systems couldn't be optimized by this approach. To remedy this deficiency, different approaches have emerged, including approximate methods. In this paper, fuzzy logic is used to continually optimize the operations. In this case, the reinforcement learning method sets the fuzzy control rules which are the principles of optimal control. Two approaches, stabilizing the pendulum and both of pendulum and cart are considered to control the pole- cart problem in this paper. The results show that the applied artificial intelligence can be used as a proper solution for the taken policy.

The study aimed at describing university students' ability in constructing mathematical proofs using logical arguments and constructing an effective teaching-learning map. The study adopted a correlational research design. An open-ended mathematics assessment involving questions on mathematical logic and construction of mathematical proofs was administered to fifteen University students of mathematics (control group) at Mountains of the Moon university. The students' responses were analyzed and their ability to construct valid mathematical proofs was measured and correctness of their solutions was discussed. A concept map for teaching-learning mathematical logic and methods of proof was developed and shared with a different group of fifteen students of mathematics (assumed to have same level of mathematical knowledge). The group was assessed using the similar assessment tool. The mean level of knowledge of mathematical logic and method of proof was from to be 2.2 (between moderate and average) for the first assessment teat and 3.4 (between average and high level), while the mean score for test 1 and test 2 were 48 and 70 respectively. The Chi square test at 0.01 level indicated a statistically significant difference between the two sets of results with p<0.05. The students' knowledge level increased as they made use of the teaching-learning map. The teaching-learning concept map was evaluated to be an effective teaching-learning guide for logic and methods of proof.

Rocky's Boots (RB), an educational game developed for use with Apple computers, is widely considered to be one of the most imaginative and engaging pieces of educational software currently available. RB presents an introduction to the logical concepts of AND, OR, and NOT. Players incorporate these concepts into arguments which are modeled as "machines." This study examined: how students responded to this cognitively challenging and complex game; if they would proceed into the game without external instruction or encouragement; if students with prior computer experience were more proficient at RB; if there was any transfer of skills from RB to linguistic logic problems (or vice versa); and if it was possible to assist students in solving the more difficult problems by providing certain general hints and rules of analysis. Findings, among others, indicate that RB is an engrossing game that students found intrinsically enjoyable and that although the style of interaction with RB differs between boys and girls, both groups showed equal levels of enthusiasm and enjoyment. Recommendations for classroom use of RB (such as offering hints to students after allowing them to explore, experiment, and solve problems) and suggestions for further study are included. (JN)

This document explores the feasibility of loans to support English adults engaging in lifelong learning. The following topics are considered: (1) the policy context (loans in higher education, individual learning accounts, and education maintenance allowances); (2) the need for financial support; (3) attitudes toward loans; (3) financial advice, financial literacy, and knowledge about the impact of debt; (4) the returns on investment in academic and vocational learning; and (5) the costs of participation. The following conclusions are reached: (1) it seems unlikely that loans will have any role in supporting students under the age of 19; (2) it is doubtful whether loans should play a significant role in supporting learners to access programs below level 3; (3) for programs at level 3 or above, the evidence about returns on investment suggests that borrowing to finance learning could be to an individual's benefit; (4) current fees in public sector part-time courses are well below the rate at which potential students seem to look to formal loan arrangements; and (5) loans would be most likely to contribute to lifelong learning in circumstances where a combination of fees and other participation costs is substantial and the program offers the prospect of high returns to students. (MN)

This paper provides both Theory and Practical of Programmable logic controller (PLC) in Automation engineering. According to various manufacturing company, the nature and useful area of PLC may be different and this paper mainly supports step by step approaching methods to be familiar with what is PLC and how PLC work. Automation purpose is the main application of PLC and the programmers or control supervisors will be able to learn easily with the four levels or steps that perform in this paper. Dr. Wai Phyo Aung"Guidelines for Approching Steps of Learning Programmable Logic Controllers in Automation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15743.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/15743/guidelines-for-approching-steps-of-learning-programmable-logic-controllers-in-automation/dr-wai-phyo-aung

Large computer-understandable proofs consist of millions of intermediate logical steps. The vast majority of such steps originate from manually selected and manually guided heuristics applied to intermediate goals. So far, machine learning has generally not been used to filter or generate these steps. In this paper, we introduce a new dataset based on Higher-Order Logic (HOL) proofs, for the purpose of developing new machine learning-based theorem-proving strategies. We make this dataset publicly available under the BSD license. We propose various machine learning tasks that can be performed on this dataset, and discuss their significance for theorem proving. We also benchmark a set of simple baseline machine learning models suited for the tasks (including logistic regression, convolutional neural networks and recurrent neural networks). The results of our baseline models show the promise of applying machine learning to HOL theorem proving.

This paper addresses the problem of learning optimal policies for satisfying signal temporal logic (STL) specifications by agents with unknown stochastic dynamics. The system is modeled as a Markov decision process, in which the states represent partitions of a continuous space and the transition probabilities are unknown. We formulate two synthesis problems where the desired STL specification is enforced by maximizing the probability of satisfaction, and the expected robustness degree, that is, a measure quantifying the quality of satisfaction. We discuss that Q-learning is not directly applicable to these problems because, based on the quantitative semantics of STL, the probability of satisfaction and expected robustness degree are not in the standard objective form of Q-learning. To resolve this issue, we propose an approximation of STL synthesis problems that can be solved via Q-learning, and we derive some performance bounds for the policies obtained by the approximate approach. The performance of the proposed method is demonstrated via simulations.

We propose to synthesize a control policy for a Markov decision process (MDP) such that the resulting traces of the MDP satisfy a linear temporal logic (LTL) property. We construct a product MDP that incorporates a deterministic Rabin automaton generated from the desired LTL property. The reward function of the product MDP is defined from the acceptance condition of the Rabin automaton. This construction allows us to apply techniques from learning theory to the problem of synthesis for LTL specifications even when the transition probabilities are not known a priori. We prove that our method is guaranteed to find a controller that satisfies the LTL property with probability one if such a policy exists, and we suggest empirically with a case study in traffic control that our method produces reasonable control strategies even when the LTL property cannot be satisfied with probability one.

Current reinforcement learning algorithms require long training periods which generally limit their applicability to small size problems. A new architecture is described which uses fuzzy rules to initialize its two neural networks: a neural network for performance evaluation and another for action selection. This architecture is applied to control of dynamic systems and it is demonstrated that it is possible to start with an approximate prior knowledge and learn to refine it through experiments using reinforcement learning.

Downloaded from Centers for Disease Control and Prevention (CDC) Youtube channel on 2025-02-02 20:38:13 https://youtube.com/watch?v=2HrG5ButP_g -------- It's fun to make fun of logic models. While some criticism is justified, much is directed at a caricature of logic models that no model fan would recognize. In this webinar we'll review why a program description is essential, how drawing a picture is the best way to describe your program, and how a simple, practical logic model is the most effective picture. We'll dissect terms like output and moderator and discuss when to include them and their value they may add to your program description. Comments on this video are allowed in accordance with our comment policy: http://www.cdc.gov/SocialMedia/Tools/CommentPolicy.html This video can also be viewed at https://www.cdc.gov/eval/videos/logic-model/LearningToLoveModel-LowRes.mp4

In May 2019, the British Columbia Teachers' Federation (BCTF) hosted a one-day seminar entitled "Questioning the Organisation for Economic Co-operation and Development (OECD) Education 2030 Program." The seminar provided a space to critically explore the Education 2030 program and the general approach to global standards through international tests. Seminar participants then attended the "OECD Education 2030 9th Informal Working Group meeting and B.C. Education Conference," hosted by the BC Ministry of Education in Vancouver. This publication is a collection of reflections on these two events by teachers, union leaders and academics. Taken together, the papers disrupt efforts to position BC as an exemplar of the type of reform promoted by the OECD. Through presentations and discussions focused on the OECD program, and looking specifically at the BC curriculum, participants questioned efforts to position BC as an exemplar of the type of reform promoted by the OECD. By thinking across these spaces, the papers work to question a "global" agenda and ask about the effects of a policy logic whereby the "local" has no choice but to adapt or be left behind in the global race towards well-being. How can the narrative being told about "who" students and teachers are, "what" they need to know, and how teaching and learning happen be disrupted? How can the assumptions behind this narrative be challenged? How can local knowledges and processes be mobilized and supported? How can solidarity be built amongst teachers nationally and internationally to speak back to emerging spaces of global governance?

We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM algorithm, the graphical EM algorithm, that runs for a class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs. The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside algorithm for PCFGs, and the one for singly connected Bayesian networks that have been developed independently in each research field. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can significantly outperform the Inside-Outside algorithm.

Virtual Environments for Training (VET) are useful tools for visualization, discovery as well as for training. VETs are based on virtual reality technique to put learners in training situations that emulate genuine situations. VETs have proven to be advantageous in putting learners into varied training situations to acquire knowledge and competencies, especially when these situations are taking place in uncontrolled circumstances, or when they are dangerous, unrealizable, or expensive to establish in reality. However individual learners find it difficult to select suitable activities for their particular situations because there is no personalized service to respond to the user needs. The solution is to generate learning activities based on learner's profile. Yet, a learner's profile may contain uncertain data (such as the desired level of difficulty, etc). This paper presents an attempt to introduce the concepts of fuzzy set theory the design of an online educational module. Such a module can deal with uncertainties in the knowledge acquisition, representation and decision making. The fuzzy logic principles are used to create the learner profile and to provide the appropriate learning activities to each learner according to his/her profile. [For the complete proceedings, see ED579335.]

Virtual Environments for Training (VET) are useful tools for visualization, discovery as well as for training. VETs are based on virtual reality technique to put learners in training situations that emulate genuine situations. VETs have proven to be advantageous in putting learners into varied training situations to acquire knowledge and competencies, especially when these situations are taking place in uncontrolled circumstances, or when they are dangerous, unrealizable, or expensive to establish in reality. However individual learners find it difficult to select suitable activities for their particular situations because there is no personalized service to respond to the user needs. The solution is to generate learning activities based on learner's profile. Yet, a learner's profile may contain uncertain data (such as the desired level of difficulty, etc). This paper presents an attempt to introduce the concepts of fuzzy set theory the design of an online educational module. Such a module can deal with uncertainties in the knowledge acquisition, representation and decision making. The fuzzy logic principles are used to create the learner profile and to provide the appropriate learning activities to each learner according to his/her profile. [For the complete proceedings, see ED579335.]

We propose to synthesize a control policy for a Markov decision process (MDP) such that the resulting traces of the MDP satisfy a linear temporal logic (LTL) property. We construct a product MDP that incorporates a deterministic Rabin automaton generated from the desired LTL property. The reward function of the product MDP is defined from the acceptance condition of the Rabin automaton. This construction allows us to apply techniques from learning theory to the problem of synthesis for LTL specifications even when the transition probabilities are not known a priori. We prove that our method is guaranteed to find a controller that satisfies the LTL property with probability one if such a policy exists, and we suggest empirically with a case study in traffic control that our method produces reasonable control strategies even when the LTL property cannot be satisfied with probability one.

This paper details the implementation of an algorithm for automatically generating a high-level knowledge network to perform commonsense reasoning, specifically with the application of robotic task repair. The network is represented using a Bayesian Logic Network (BLN) (Jain, Waldherr, and Beetz 2009), which combines a set of directed relations between abstract concepts, including IsA, AtLocation, HasProperty, and UsedFor, with a corresponding probability distribution that models the uncertainty inherent in these relations. Inference over this network enables reasoning over the abstract concepts in order to perform appropriate object substitution or to locate missing objects in the robot's environment. The structure of the network is generated by combining information from two existing knowledge sources: ConceptNet (Speer and Havasi 2012), and WordNet (Miller 1995). This is done in a "situated" manner by only including information relevant a given context. Results show that the generated network is able to accurately predict object categories, locations, properties, and affordances in three different household scenarios.

The booklet describes ways in which a logic teacher learned to accommodate learning disabled college students. Information on structuring the course includes general principles (such as clear expectations and a variety of learning modalities), considerations in identifying LD students, and use of a detailed course syllabus. He reviews a typical class session with procedures that are helpful to LD and non-LD students alike. Miscellaneous suggestions are offered concerning pictures and diagrams, drill, examinations, and computer assisted instruction. Concluding remarks focus on the importance of helping students apply logic to everyday situations. Appended material includes exercises to identify students with possible LD and a course syllabus. (CL)

The booklet describes ways in which a logic teacher learned to accommodate learning disabled college students. Information on structuring the course includes general principles (such as clear expectations and a variety of learning modalities), considerations in identifying LD students, and use of a detailed course syllabus. He reviews a typical class session with procedures that are helpful to LD and non-LD students alike. Miscellaneous suggestions are offered concerning pictures and diagrams, drill, examinations, and computer assisted instruction. Concluding remarks focus on the importance of helping students apply logic to everyday situations. Appended material includes exercises to identify students with possible LD and a course syllabus. (CL)

According to the Internet World Stats (2010), the growth rate of internet usage in the world is 444.8 % from 2000 to 2010. Since the number of internet users is rapidly increasing with each passed year, e-learning is often identified with web-based learning. The institutions, which deliver e-learning service via the use of computer and internet, are responsible to choose the most suitable e-learning service provider for effective distance education. The purpose of this research is to identify the e-learning design requirements and to select the most suitable e-learning service provider. In this research, fuzzy logic-based Quality Function Deployment (QFD) was employed. A questionnaire was conducted in order to collect the data from a group of experts who were selected on the basis of their knowledge and expertise in related industry. By using the Converting Fuzzy data into Crisp Scores (CFCS) technique, the collected data was defuzzified. Then, the critical success factors of e-learning service providers were identified. As a result, fuzzy logic-based QFD was utilized for the selection of the e-learning service providers. (Contains 2 figures and 5 tables.)

This paper introduces a conceptual model for supporting the continued development of service-learning as a pedagogy of engagement. A logic diagram is used to facilitate understanding of service-learning. The model illustrates the (a) complex elements involved in creating or sustaining a strong program, (b) potential tensions within the field, and (c) evaluation requirements at the level of a program or campus. The logic model also identifies tensions and issues that merit ongoing discussion amongst those committed to the continued development of service-learning in higher education. (Contains 2 figures and 7 notes.)

Advances in computers and multimedia technology have changed traditional methods for learning and skills training. Online learning continues to play a major success of any academic program. Such learning can personalize learning needs for students, it can provide an environment where virtual reality techniques are used to create interactive interfaces and real-time software can monitor every response made by the user. This paper presents an attempt to introduce the concepts of fuzzy set theory in the design of an online educational module. Such a module can deal with uncertainties in the knowledge acquisition, representation and decision making. The fuzzy logic principles are used in creating the learner model and to provide the appropriate teaching material to each learner according to his/her learning level.

Two courses on logic gates were administered to samples of airmen in Basic Military Training to evaluate the relative validities of ASVAB and LAMP tests in predicting individual differences in the acquisition of declarative knowledge (measured by the accuracy in solving problems) and the development of procedural skill (measured by the time required to solve problems). In general, the LAMP tests added about 20% unique valid variance to the ASVAB tests in predicting these criteria. Furthermore, for both criteria, the LAMP tests alone accounted for more variance than did the ASVAB tests alone. Analyses indicated that the advantage of the LAMP tests was not due to concurrency effects, nor was it obtained at the expense of excessive testing time. Although LAMP tests and the criterion tests used common methods, arguments are presented contending that most of the covariance associated with common methods was meaningful to the task being learned and performed.

Many real-world problems involve data that both have complex structures and uncertainty. Statistical relational learning (SRL) is an emerging area of research that addresses the problem of learning from these noisy structured/ relational data. Markov logic networks (MLNs), sets of weighted rstorder logic formulae, are a simple but powerful SRL formalism that generalizes both rst-order logic and Markov networks. MLNs have been successfully applied to a variety of real-world problems ranging from extraction knowledge from text to visual event recognition. Most of the existing learning algorithms for MLNs are in the generative setting: they try to learn a model that is equally capable of predicting the values of all variables given an arbitrary set of evidence; and they do not scale to problems with thousands of examples. However, many real-world problems in structured/relational data are discriminativewhere the variables are divided into two disjoint sets input and output, and the goal is to correctly predict the values of the output variables given evidence data about the input variables. In addition, these problems usually involve data that have thousands of examples. Thus, it is important to develop new discriminative learning methods for MLNs that are more accurate and more scalable, which are the topics addressed in this thesis. First, we present a new method that discriminatively learns both the structure and parameters for a special class of MLNs where all the clauses are non-recursive ones. Non-recursive clauses arise in many learning problems in Inductive Logic Programming. To further improve the predictive accuracy we propose a max-margin approach to learning weights for MLNs. Then to address the issue of scalability, we present CDA, an online max-margin weight learning algorithm for MLNs.

Natural logic offers a powerful relational conception of meaning that is a natural counterpart to distributed semantic representations, which have proven valuable in a wide range of sophisticated language tasks. However, it remains an open question whether it is possible to train distributed representations to support the rich, diverse logical reasoning captured by natural logic. We address this question using two neural network-based models for learning embeddings: plain neural networks and neural tensor networks. Our experiments evaluate the models' ability to learn the basic algebra of natural logic relations from simulated data and from the WordNet noun graph. The overall positive results are promising for the future of learned distributed representations in the applied modeling of logical semantics.

Two courses on logic gates were administered to 1,191 male and female recruits in Basic Military Training in 3 studies (448, 431, and 312 subjects, respectively) to evaluate the relative validities of the Armed Services Vocational Aptitude Battery (ASVAB) and the Learning Abilities Measurement Program (LAMP) tests in predicting individual differences in the acquisition of declarative knowledge (measured by the accuracy in solving problems) and the development of procedural skill (measured by the time required to solve problems). In general, the LAMP tests added about 20 percent unique valid variance to the ASVAB tests in predicting these criteria. Furthermore, for both criteria, the LAMP test alone accounted for more variance than did the ASVAB tests alone. Analyses indicate that the advantage of the LAMP tests was neither due to concurrency effects, nor obtained at the expense of excessive testing time. Although LAMP tests and the criterion tests used common methods, arguments are presented contending that most of the covariance associated with common methods was meaningful to the task being learned and performed. Thirty tables in the text and six more in two appendixes present study findings. (Contains 22 references.) (Author/SLD)

This article is from BMC Bioinformatics , volume 15 . Abstract Background: Cancer patient's outcome is written, in part, in the gene expression profile of the tumor. We previously identified a 62-probe sets signature (NB-hypo) to identify tissue hypoxia in neuroblastoma tumors and showed that NB-hypo stratified neuroblastoma patients in good and poor outcome [1]. It was important to develop a prognostic classifier to cluster patients into risk groups benefiting of defined therapeutic approaches. Novel classification and data discretization approaches can be instrumental for the generation of accurate predictors and robust tools for clinical decision support. We explored the application to gene expression data of Rulex, a novel software suite including the Attribute Driven Incremental Discretization technique for transforming continuous variables into simplified discrete ones and the Logic Learning Machine model for intelligible rule generation. Results: We applied Rulex components to the problem of predicting the outcome of neuroblastoma patients on the bases of 62 probe sets NB-hypo gene expression signature. The resulting classifier consisted in 9 rules utilizing mainly two conditions of the relative expression of 11 probe sets. These rules were very effective predictors, as shown in an independent validation set, demonstrating the validity of the LLM algorithm applied to microarray data and patients' classification. The LLM performed as efficiently as Prediction Analysis of Microarray and Support Vector Machine, and outperformed other learning algorithms such as C4.5. Rulex carried out a feature selection by selecting a new signature (NB-hypo-II) of 11 probe sets that turned out to be the most relevant in predicting outcome among the 62 of the NB-hypo signature. Rules are easily interpretable as they involve only few conditions.Furthermore, we demonstrate that the application of a weighted classification associated with the rules improves the classification of poorly represented classes. Conclusions: Our findings provided evidence that the application of Rulex to the expression values of NB-hypo signature created a set of accurate, high quality, consistent and interpretable rules for the prediction of neuroblastoma patients' outcome. We identified the Rulex weighted classification as a flexible tool that can support clinical decisions. For these reasons, we consider Rulex to be a useful tool for cancer classification from microarray gene expression data.

While grasps must satisfy the grasping stability criteria, good grasps depend on the specific manipulation scenario: the object, its properties and functionalities, as well as the task and grasp constraints. In this paper, we consider such information for robot grasping by leveraging manifolds and symbolic object parts. Specifically, we introduce a new probabilistic logic module to first semantically reason about pre-grasp configurations with respect to the intended tasks. Further, a mapping is learned from part-related visual features to good grasping points. The probabilistic logic module makes use of object-task affordances and object/task ontologies to encode rules that generalize over similar object parts and object/task categories. The use of probabilistic logic for task-dependent grasping contrasts with current approaches that usually learn direct mappings from visual perceptions to task-dependent grasping points. We show the benefits of the full probabilistic logic pipeline experimentally and on a real robot.

A reflection about two classroom experiences is presented in the attempt to incorporate the Logic of Sense into the notion of inquiry for learning. The author used the method of Experimentation introduced by Deleuze and Guattari, who based its principles on philosophical conceptions by Baruch Spinoza. The first experience is conducted with students from the subject called Reading and Speaking workshop from the BA in English Teaching at Tolima University. The researcher designed a reading protocol which, in an initial stage, allowed students to comprehend the texts assigned for the class. Afterward, this protocol enabled students to reach levels of application and evaluation through the formulation of questions based on the Series of the Logic of Sense. The second experience was carried out with students from the Masters in English Didactics from the same University. The same reading protocol format was used; but, this time the questions constructed let students envision perspectives in the design of new curricular proposals.

Systems analysis & design

This article is from BMC Bioinformatics , volume 14 . Abstract Background: Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. Results: We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate “regulates”, starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a pairwise SVM while providing relevant insights on the predictions. Conclusions: The numerical studies show that MLN achieves very good predictive performance while opening the door to some interpretability of the decisions. Besides the ability to suggest new regulations, such an approach allows to cross-validate experimental data with existing knowledge.