How Minds Work: A Cognitive Theory of Everything - a Tutorial on the IDA Model of Cognition

The IDA model of cognition is a fully integrated artificial cognitive system reaching across the full spectrum of cognition, from low-level perception/action to high-level reasoning. Extensively based on empirical data, it accurately reflects the full range of cognitive processes found in natural cognitive systems. As a source of plausible explanations for very many cognitive processes, the IDA model provides an ideal tool to think with about how minds work. This online tutorial offers a reasonably full account of the IDA conceptual model, including background material. It also provides a high-level account of the underlying computational “mechanisms of mind” that constitute the IDA computational model.

A Conceptual Model to Combine Creativity Techniques with Fuzzy Cognitive Maps for Enhanced Knowledge Management

This chapter introduces a conceptual model to combine creativity techniques with fuzzy cognitive maps (FCMs) and aims to support knowledge management methods by improving expert knowledge acquisition and aggregation. The aim of the conceptual model is to represent acquired knowledge in a manner that is as computer-understandable as possible with the intention of developing automated reasoning in the future as part of intelligent information systems. The formal represented knowledge thus may provide businesses with intelligent information integration. To this end, we introduce and evaluate various creativity techniques with a list of attributes to define the most suitable to combine with FCMs. This proposed combination enables enhanced knowledge management through the acquisition and representation of expert knowledge with FCMs. Our evaluation indicates that the creativity technique known as mind mapping is the most suitable technique in our set. Finally, a scenario from stakeholder management demonstrates the combination of mind mapping with FCMs as an integrated system.

Improving self-control by practicing logical reasoning

We tested the hypothesis that practicing logical reasoning can improve self-control. In an experimental training study (N = 49 undergraduates), for one week participants engaged in daily mental exercises with or without the requirement to practice logical reasoning. Participants in the logic group showed improvements in self-control, as revealed by anagram performance after a depleting self-control task. The benefits of the intervention were short-lived; participants in the two groups performed similarly just one week after the intervention had ended. We discuss the findings with respect to the strength model of self-control and consider possible benefits of regular cognitive challenges in education.

Exploring the validity and reliability of a questionnaire for evaluating virtual patient design with a special emphasis on fostering clinical reasoning

Background: Virtual patients (VPs) are increasingly used to train clinical reasoning. So far, no validated evaluation instruments for VP design are available. Aims: We examined the validity of an instrument for assessing the perception of VP design by learners. Methods: Three sources of validity evidence were examined: (i) Content was examined based on theory of clinical reasoning and an international VP expert team. (ii) The response process was explored in think-aloud pilot studies with medical students and in content analyses of free text questions accompanying each item of the instrument. (iii) Internal structure was assessed by exploratory factor analysis (EFA) and inter-rater reliability by generalizability analysis. Results: Content analysis was reasonably supported by the theoretical foundation and the VP expert team. The think-aloud studies and analysis of free text comments supported the validity of the instrument. In the EFA, using 2547 student evaluations of a total of 78 VPs, a three-factor model showed a reasonable fit with the data. At least 200 student responses are needed to obtain a reliable evaluation of a VP on all three factors. Conclusion: The instrument has the potential to provide valid information about VP design, provided that many responses per VP are available.

An Economic Model of Fair Use

The doctrine of fair use allows unauthorized copying of original works of art, music, and literature for limited purposes like criticism, research, and education, based on the rationale that copyright holders would consent to such uses if bargaining were possible. This paper develops the first formal analysis of fair use in an effort to derive the efficient legal standard for applying the doctrine. The model interprets copies and originals as differentiated products and defines fair use as a threshold separating permissible copying from infringement. Application of the analysis to several key cases (including the recent Napster case) shows that this interpretation is consistent with actual legal reasoning. The analysis also underscores the role of technology in shaping the efficient scope of fair use.

Probabilistic reasoning with a bayesian DNA device based on strand displacement

We present a computing model based on the DNA strand displacement technique which performs Bayesian inference. The model will take single stranded DNA as input data, representing the presence or absence of a specific molecular signal (evidence). The program logic encodes the prior probability of a disease and the conditional probability of a signal given the disease playing with a set of different DNA complexes and their ratios. When the input and program molecules interact, they release a different pair of single stranded DNA species whose relative proportion represents the application of Bayes? Law: the conditional probability of the disease given the signal. The models presented in this paper can empower the application of probabilistic reasoning in genetic diagnosis in vitro.

B2DI a bayesian BDI agent model with causal belief updating based on MSBN

In this paper, we introduce B2DI model that extends BDI model to perform Bayesian inference under uncertainty. For scalability and flexibility purposes, Multiply Sectioned Bayesian Network (MSBN) technology has been selected and adapted to BDI agent reasoning. A belief update mechanism has been defined for agents, whose belief models are connected by public shared beliefs, and the certainty of these beliefs is updated based on MSBN. The classical BDI agent architecture has been extended in order to manage uncertainty using Bayesian reasoning. The resulting extended model, so-called B2DI, proposes a new control loop. The proposed B2DI model has been evaluated in a network fault diagnosis scenario. The evaluation has compared this model with two previously developed agent models. The evaluation has been carried out with a real testbed diagnosis scenario using JADEX. As a result, the proposed model exhibits significant improvements in the cost and time required to carry out a reliable diagnosis.

AALUMO: a user model ontology for ambient assisted living services supported in next-generation networks

Ambient Assisted Living (AAL) services are emerging as context-awareness solutions to support elderly people?s autonomy. The context-aware paradigm makes applications more user-adaptive. In this way, context and user models expressed in ontologies are employed by applications to describe user and environment characteristics. The rapid advance of technology allows creating context server to relieve applications of context reasoning techniques. Specifically, the Next Generation Networks (NGN) provides by means of the presence service a framework to manage the current user's state as well as the user's profile information extracted from Internet and mobile context. This paper propose a user modeling ontology for AAL services which can be deployed in a NGN environment with the aim at adapting their functionalities to the elderly's context information and state.

Probabilistic reasoning with an enzyme-driven DNA device

We present a biomolecular probabilistic model driven by the action of a DNA toolbox made of a set of DNA templates and enzymes that is able to perform Bayesian inference. The model will take single-stranded DNA as input data, representing the presence or absence of a specific molecular signal (the evidence). The program logic uses different DNA templates and their relative concentration ratios to encode the prior probability of a disease and the conditional probability of a signal given the disease. When the input and program molecules interact, an enzyme-driven cascade of reactions (DNA polymerase extension, nicking and degradation) is triggered, producing a different pair of single-stranded DNA species. Once the system reaches equilibrium, the ratio between the output species will represent the application of Bayes? law: the conditional probability of the disease given the signal. In other words, a qualitative diagnosis plus a quantitative degree of belief in that diagno- sis. Thanks to the inherent amplification capability of this DNA toolbox, the resulting system will be able to to scale up (with longer cascades and thus more input signals) a Bayesian biosensor that we designed previously.

Automated end user-centred adaptation of web components through automated description logic-based reasoning

Context: This paper addresses one of the major end-user development (EUD) challenges, namely, how to pack today?s EUD support tools with composable elements. This would give end users better access to more components which they can use to build a solution tailored to their own needs. The success of later end-user software engineering (EUSE) activities largely depends on how many components each tool has and how adaptable components are to multiple problem domains. Objective: A system for automatically adapting heterogeneous components to a common development environment would offer a sizeable saving of time and resources within the EUD support tool construction process. This paper presents an automated adaptation system for transforming EUD components to a standard format. Method: This system is based on the use of description logic. Based on a generic UML2 data model, this description logic is able to check whether an end-user component can be transformed to this modeling language through subsumption or as an instance of the UML2 model. Besides it automatically finds a consistent, non-ambiguous and finite set of XSLT mappings to automatically prepare data in order to leverage the component as part of a tool that conforms to the target UML2 component model. Results: The proposed system has been successfully applied to components from four prominent EUD tools. These components were automatically converted to a standard format. In order to validate the proposed system, rich internet applications (RIA) used as an operational support system for operators at a large services company were developed using automatically adapted standard format components. These RIAs would be impossible to develop using each EUD tool separately. Conclusion: The positive results of applying our system for automatically adapting components from current tool catalogues are indicative of the system?s effectiveness. Use of this system could foster the growth of web EUD component catalogues, leveraging a vast ecosystem of user-centred SaaS to further current EUSE trends.

Applying qualitative reasoning to finance

Qualitative reasoning has traditionally been applied in the domain of physical systems, where there are well established and understood laws governing the behaviour of each `component' in the system. Such application has shown that it is possible to produce models which can be used for explaining and predicting the behaviour of physical phenomena and also trouble-shooting. The principles underlying the theory ensure that the models are robust and exhibit consistent behaviour under all conditions. This research examines the validity of applying the theory in the financial domain where such laws may not exist or if they do, may not be universally applicable. In particular, it investigates how far these principles and techniques may be applied in the construction of financial analysis models. Because of the inherent differences in the nature of these two domains, it is argued that a different qualitative value system ought to be employed. The dissertation enlarges on the constraints this places on model descriptions and the effect it may have on the power and usefulness of the resulting models. It also describes the implementation of a system that investigates the implications of applying this theory by way of testing it on situations drawn from both text-books and published financial information.

The application of methods of uncertain reasoning to the biological classification of river water quality

This thesis presents an investigation into the application of methods of uncertain reasoning to the biological classification of river water quality. Existing biological methods for reporting river water quality are critically evaluated, and the adoption of a discrete biological classification scheme advocated. Reasoning methods for managing uncertainty are explained, in which the Bayesian and Dempster-Shafer calculi are cited as primary numerical schemes. Elicitation of qualitative knowledge on benthic invertebrates is described. The specificity of benthic response to changes in water quality leads to the adoption of a sensor model of data interpretation, in which a reference set of taxa provide probabilistic support for the biological classes. The significance of sensor states, including that of absence, is shown. Novel techniques of directly eliciting the required uncertainty measures are presented. Bayesian and Dempster-Shafer calculi were used to combine the evidence provided by the sensors. The performance of these automatic classifiers was compared with the expert's own discrete classification of sampled sites. Variations of sensor data weighting, combination order and belief representation were examined for their effect on classification performance. The behaviour of the calculi under evidential conflict and alternative combination rules was investigated. Small variations in evidential weight and the inclusion of evidence from sensors absent from a sample improved classification performance of Bayesian belief and support for singleton hypotheses. For simple support, inclusion of absent evidence decreased classification rate. The performance of Dempster-Shafer classification using consonant belief functions was comparable to Bayesian and singleton belief. Recommendations are made for further work in biological classification using uncertain reasoning methods, including the combination of multiple-expert opinion, the use of Bayesian networks, and the integration of classification software within a decision support system for water quality assessment.

A method for automatically eliciting node weights in a hierarchical knowledge-based structure for reasoning with uncertainty

Hierarchical knowledge structures are frequently used within clinical decision support systems as part of the model for generating intelligent advice. The nodes in the hierarchy inevitably have varying influence on the decisionmaking processes, which needs to be reflected by parameters. If the model has been elicited from human experts, it is not feasible to ask them to estimate the parameters because there will be so many in even moderately-sized structures. This paper describes how the parameters could be obtained from data instead, using only a small number of cases. The original method [1] is applied to a particular web-based clinical decision support system called GRiST, which uses its hierarchical knowledge to quantify the risks associated with mental-health problems. The knowledge was elicited from multidisciplinary mental-health practitioners but the tree has several thousand nodes, all requiring an estimation of their relative influence on the assessment process. The method described in the paper shows how they can be obtained from about 200 cases instead. It greatly reduces the experts’ elicitation tasks and has the potential for being generalised to similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.

A semantic web environment for component model

Component-based development (CBD) has become an important emerging topic in the software engineering field. It promises long-sought-after benefits such as increased software reuse, reduced development time to market and, hence, reduced software production cost. Despite the huge potential, the lack of reasoning support and development environment of component modeling and verification may hinder its development. Methods and tools that can support component model analysis are highly appreciated by industry. Such a tool support should be fully automated as well as efficient. At the same time, the reasoning tool should scale up well as it may need to handle hundreds or even thousands of components that a modern software system may have. Furthermore, a distributed environment that can effectively manage and compose components is also desirable. In this paper, we present an approach to the modeling and verification of a newly proposed component model using Semantic Web languages and their reasoning tools. We use the Web Ontology Language and the Semantic Web Rule Language to precisely capture the inter-relationships and constraints among the entities in a component model. Semantic Web reasoning tools are deployed to perform automated analysis support of the component models. Moreover, we also proposed a service-oriented architecture (SOA)-based semantic web environment for CBD. The adoption of Semantic Web services and SOA make our component environment more reusable, scalable, dynamic and adaptive.

Reasoning Methods for Designing and Surveying Relationships Described by Sets of Functional Constraints

This work is supported by the Hungarian Scientific Research Fund (OTKA), grant T042706.