A Ligand predication tool based on modeling and reasoning with imprecise probabilistic knowledge

Ligand prediction has been driven by a fundamental desire to understand more about how biomolecules recognize their ligands and by the commercial imperative to develop new drugs. Most of the current available software systems are very complex and time-consuming to use. Therefore, developing simple and efficient tools to perform initial screening of interesting compounds is an appealing idea. In this paper, we introduce our tool for very rapid screening for likely ligands (either substrates or inhibitors) based on reasoning with imprecise probabilistic knowledge elicited from past experiments. Probabilistic knowledge is input to the system via a user-friendly interface showing a base compound structure. A prediction of whether a particular compound is a substrate is queried against the acquired probabilistic knowledge base and a probability is returned as an indication of the prediction. This tool will be particularly useful in situations where a number of similar compounds have been screened experimentally, but information is not available for all possible members of that group of compounds. We use two case studies to demonstrate how to use the tool.

Episodic Reasoning for Vision-Based Human Action Recognition

Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, human action recognition cannot be successfully accomplished by only analyzing body postures. On the contrary, this task should be supported by profound knowledge of human agency nature and its tight connection to the reasons and motivations that explain it. The combination of this knowledge and the knowledge about how the world works is essential for recognizing and understanding human actions without committing common-senseless mistakes. This work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. This work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning.

Effectiveness of instructional strategies based on schema theory on attention deficit disorder children

A review of the literature reveals that there are a number of children in the educational system who are characterized by Attention Deficit Disorder. Further review of the literature reveals that there are information processing programs which have had some success in increasing the learning of these children. Currently, an information processing program which is based on schema theory is being implemented in Lincoln County. Since schema theory based programs build structural, conditional, factual, and procedural schemata which assist the learner in attending to salient factors, learning should be increased. Thirty-four children were selected from a random sampling of Grade Seven classes in Lincoln County. Seventeen of these children were identified by the researcher and classroom teacher as being characterized by Attention Deficit Disorder. From the remaining population, 17 children who were not characterized by Attention Deficit Disorder were randomly selected. The data collected were compared using independent t-tests, paired t-tests, and correlation analysis. Significant differences were found in all cases. The Non-Attention Deficit Disorder children scored significantly higher on all the tests but the Attention Defici t Disorder children had a significantly higher ratio of gain between the pretests and posttests.

CARSA : A context-aware reasoning-based service agent model for AI planning of web service compostion

In order to achieve automatic and more intelligent service composition, dynamic description logic (DDL) is proposed and utilized as one emerging logic-level solution. However, reasoning optimization and utilization in such DDL-related solutions is still an open problem. In this paper, we propose the context-aware reasoning-based service agent model (CARSA) which exploits the relationships among different service consumers and providers, together with the corresponding optimization approach to strengthen the effectiveness of Web service composition. Through the model, two reasoning optimization methods are proposed based on the substitute relationship and the dependency relationship, respectively, so irrelevant actions can be filtered out of the reasoning space before the DDL reasoning process is carried out. The case study and experimental analysis demonstrates the capability of the proposed approach.

A non-linear programming-based similarity reasoning scheme for modelling of monotonicity-preserving multi-input fuzzy inference systems

In this paper, the zero-order Sugeno Fuzzy Inference System (FIS) that preserves the monotonicity property is studied. The sufficient conditions for the zero-order Sugeno FIS model to satisfy the monotonicity property are exploited as a set of useful governing equations to facilitate the FIS modelling process. The sufficient conditions suggest a fuzzy partition (at the rule antecedent part) and a monotonically-ordered rule base (at the rule consequent part) that can preserve the monotonicity property. The investigation focuses on the use of two Similarity Reasoning (SR)-based methods, i.e., Analogical Reasoning (AR) and Fuzzy Rule Interpolation (FRI), to deduce each conclusion separately. It is shown that AR and FRI may not be a direct solution to modelling of a multi-input FIS model that fulfils the monotonicity property, owing to the difficulty in getting a set of monotonically-ordered conclusions. As such, a Non-Linear Programming (NLP)-based SR scheme for constructing a monotonicity-preserving multi-input FIS model is proposed. In the proposed scheme, AR or FRI is first used to predict the rule conclusion of each observation. Then, a search algorithm is adopted to look for a set of consequents with minimized root means square errors as compared with the predicted conclusions. A constraint imposed by the sufficient conditions is also included in the search process. Applicability of the proposed scheme to undertaking fuzzy Failure Mode and Effect Analysis (FMEA) tasks is demonstrated. The results indicate that the proposed NLP-based SR scheme is useful for preserving the monotonicity property for building a multi-input FIS model with an incomplete rule base.

Building monotonicity-preserving fuzzy inference models with optimization-based similarity reasoning and a monotonicity index

In this paper, a novel approach to building a Fuzzy Inference System (FIS) that preserves the monotonicity property is proposed. A new fuzzy re-labeling technique to re-label the consequents of fuzzy rules in the database (before the Similarity Reasoning process) and a monotonicity index for use in FIS modeling are introduced. The proposed approach is able to overcome several restrictions in our previous work that uses mathematical conditions in building monotonicity-preserving FIS models. Here, we show that the proposed approach is applicable to different FIS models, which include the zero-order Sugeno FIS and Mamdani models. Besides, the proposed approach can be extended to undertake problems related to the local monotonicity property of FIS models. A number of examples to demonstrate the usefulness of the proposed approach are presented. The results indicate the usefulness of the proposed approach in constructing monotonicity-preserving FIS models.

Using inquiry-based learning to develop and assess statistical reasoning

When learning, teaching and assessments of statistical content are based on an inquiry cycle with contextual linkages, then this results in improved learner performances. If assessment questions in statistics are linked conceptually within an appropriate context, as opposed to being fragmented, then improved learner performances in statistical reasoning results.

An evolutionary-based similarity reasoning scheme for monotonic multi-input fuzzy inference systems

In this paper, an Evolutionary-based Similarity Reasoning (ESR) scheme for preserving the monotonicity property of the multi-input Fuzzy Inference System (FIS) is proposed. Similarity reasoning (SR) is a useful solution for undertaking the incomplete rule base problem in FIS modeling. However, SR may not be a direct solution to designing monotonic multi-input FIS models, owing to the difficulty in getting a set of monotonically-ordered conclusions. The proposed ESR scheme, which is a synthesis of evolutionary computing, sufficient conditions, and SR, provides a useful solution to modeling and preserving the monotonicity property of multi-input FIS models. A case study on Failure Mode and Effect Analysis (FMEA) is used to demonstrate the effectiveness of the proposed ESR scheme in undertaking real world problems that require the monotonicity property of FIS models.

Developing and using an S3R model to analyze reasoning in web-based cross-national exchanges on sustainability

Within the increasing body of research that examines students' reasoning on socioscientific issues, we consider in particular student reasoning concerning acute, open-ended questions that bring out the complexities and uncertainties embedded in ill-structured problems. In this paper, we propose a socioscientific sustainability reasoning (S3R) model to analyze students' reasoning exchanges on environmental socially acute questions (ESAQs). The paper describes the development of an epistemological analysis of how sustainability perspectives can be integrated into socioscientific reasoning, which emphasizes the need for S3R to be both grounded in context and collective. We argue the complexity of ESAQs requires a consideration of multiple dimensions that form the basis of our S3R analysis model: problematization, interactions, knowledge, uncertainties, values, and governance. For each dimension, in the model we have identified indicators of four levels of complexity. We investigated the usefulness of the model in identifying improvements in reasoning that flow from cross-national web-based exchanges between groups of French and Australian students, concerning a local and a global ESAQ. The S3R model successfully captured the nature of reasoning about socioscientific sustainability issues, with the collective negotiation of multiple forms of knowledge as a key characteristic in improving reasoning levels. The paper provides examples of collaborative argumentation in collective texts (wikis) to illustrate the various levels of reasoning in each dimension, and diagrammatic representation of the evolution of collective reflections. We observe that a staged process of construction and confrontation, involving groups representing to some extent different cultural and contextual stances, is powerful in eliciting reasoned argument of enhanced quality. © 2014 Wiley Periodicals, Inc.

Building fuzzy inference systems with similarity reasoning : NSGAII-based fuzzy rule selection and evidential functions

In our previous investigations, two Similarity Reasoning (SR)-based frameworks for tackling real-world problems have been proposed. In both frameworks, SR is used to deduce unknown fuzzy rules based on similarity of the given and unknown fuzzy rules for building a Fuzzy Inference System (FIS). In this paper, we further extend our previous findings by developing (1) a multi-objective evolutionary model for fuzzy rule selection; and (2) an evidential function to facilitate the use of both frameworks. The Non-Dominated Sorting Genetic Algorithms-p (NSGA-p) is adopted for fuzzy rule selection, in accordance with the Pareto optimal criterion. Besides that, two new evidential functions are developed, whereby given fuzzy rules are considered as evidence. Simulated and benchmark examples are included to demonstrate the applicability of these suggestions. Positive results were obtained.