Representation of maxitive measures: an overview

Idempotent integration is an analogue of Lebesgue integration where $\sigma$-maxitive measures replace $\sigma$-additive measures. In addition to reviewing and unifying several Radon--Nikodym like theorems proven in the literature for the idempotent integral, we also prove new results of the same kind.Comment: 40 page

What is Computational Intelligence and where is it going?

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed

THINGS FROM THE FUTURE How can we crowdsource innovation foresight with games?

In the current world uncertainty is more dominant than it used to be. One of the key forces for constant change is innovation. Innovations can be radical and create surprising effects. Can there be ways of anticipating these unforeseen effects of innovation? Or can the course of future innovations be managed somehow? Innovation foresight processes are required to communicate between different stakeholders on an extensive scale to be able to build comprehensive and understandable future options. Knowledge on future and innovation is no more the exclusive right of experts. This study tries to find new ways of engaging people with the innovation foresight work as well as get new audiences to participate in it. Games and crowdsourcing are two possible solutions to this. Theories covering innovation, foreisght and crowdsourcing are plentiful but scattered, and do not form a coherent framework for innovation foresight. Study is approaching the research topic from two perspectives: what kind of innovation foresight knowledge can we create with games, and what innovation foresight activities can we crowdsource with games? For these targets study has used two different methods, an innovation game case study experiment and a questionnaire targeted to Finnish innovation experts. Game case study consisted of a foresight analysis of 310 ”future thing” ideas generated with an innovation card game. The results revealed that games can enhance the creativity of the players and generate many unexpected uses of future technologies and services. Ideas were also rich with future hopes and fears and they had multidimensional content including different PESTE-variables. Questionnaire was targeted to map views related to the usability of games in different phases of the innovation foresight process. According to responses gaming can be used to observe weak signals, to form wild cards, perceive hopes and fears, and to develop new visions for the future. But games are not seen as suitable for decision-making nor forecasting future trends. Crowdsourcing can enhance the ”crowd wisdom” of the foresight process. Crowd wisdom means that groups are often smarter than the smartest people in them. This phenomenon is based on the thought that “no one knows everything, but everyone knows something”. The challenge in crowdsourcing is to motivate people to participate and engage. Games can be a powerful solution to innovation foresight motivation challenge, and they may also generate different solutions than other methods. But games cannot replace the foresight process. To subject foresight to games and gamification would take too many resources, be expensive, difficult to manage, and results would be risky. Crowdsourcing innovation foresight can often be carried out more effectively when using existing social media platforms such as Facebook, Twitter etc. instead of games. In any case, crowd wisdom is too valuable resource not to be exploited in foresight.siirretty Doriast

Construction of a support tool for the design of the activity structures based computer system architectures

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This thesis is a reapproachment of diverse design concepts, brought to bear upon the computer system engineering problem of identification and control of highly constrained multiprocessing (HCM) computer machines. It contributes to the area of meta/general systems methodology, and brings a new insight into the design formalisms, and results afforded by bringing together various design concepts that can be used for the construction of highly constrained computer system architectures. A unique point of view is taken by assuming the process of identification and control of HCM computer systems to be the process generated by the Activity Structures Methodology (ASM). The research in ASM has emerged from the Neuroscience research, aiming at providing the techniques for combining the diverse knowledge sources that capture the 'deep knowledge' of this application field in an effective formal and computer representable form. To apply the ASM design guidelines in the realm of the distributed computer system design, we provide new design definitions for the identification and control of such machines in terms of realisations. These realisation definitions characterise the various classes of the identification and control problem. The classes covered consist of: 1. the identification of the designer activities, 2. the identification and control of the machine's distributed structures of behaviour, 3. the identification and control of the conversational environment activities (i.e. the randomised/ adaptive activities and interactions of both the user and the machine environments), 4. the identification and control of the substrata needed for the realisation of the machine, and 5. the identification of the admissible design data, both user-oriented and machineoriented, that can force the conversational environment to act in a self-regulating manner. All extent results are considered in this context, allowing the development of both necessary conditions for machine identification in terms of their distributed behaviours as well as the substrata structures of the unknown machine and sufficient conditions in terms of experiments on the unknown machine to achieve the self-regulation behaviour. We provide a detailed description of the design and implementation of the support software tool which can be used for aiding the process of constructing effective, HCM computer systems, based on various classes of identification and control. The design data of a highly constrained system, the NUKE, are used to verify the tool logic as well as the various identification and control procedures. Possible extensions as well as future work implied by the results are considered.Government of Ira

Interval and Possibilistic Methods for Constraint-Based Metabolic Models

This thesis is devoted to the study and application of constraint-based metabolic models. The objective was to find simple ways to handle the difficulties that arise in practice due to uncertainty (knowledge is incomplete, there is a lack of measurable variables, and those available are imprecise). With this purpose, tools have been developed to model, analyse, estimate and predict the metabolic behaviour of cells. The document is structured in three parts. First, related literature is revised and summarised. This results in a unified perspective of several methodologies that use constraint-based representations of the cell metabolism. Three outstanding methods are discussed in detail, network-based pathways analysis (NPA), metabolic flux analysis (MFA), and flux balance analysis (FBA). Four types of metabolic pathways are also compared to clarify the subtle differences among them. The second part is devoted to interval methods for constraint-based models. The first contribution is an interval approach to traditional MFA, particularly useful to estimate the metabolic fluxes under data scarcity (FS-MFA). These estimates provide insight on the internal state of cells, which determines the behaviour they exhibit at given conditions. The second contribution is a procedure for monitoring the metabolic fluxes during a cultivation process that uses FS-MFA to handle uncertainty. The third part of the document addresses the use of possibility theory. The main contribution is a possibilistic framework to (a) evaluate model and measurements consistency, and (b) perform flux estimations (Poss-MFA). It combines flexibility on the assumptions and computational efficiency. Poss-MFA is also applied to monitoring fluxes and metabolite concentrations during a cultivation, information of great use for fault-detection and control of industrial processes. Afterwards, the FBA problem is addressed.Llaneras Estrada, F. (2011). Interval and Possibilistic Methods for Constraint-Based Metabolic Models [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10528Palanci

Learning Possibilistic Logic Theories

Vi tar opp problemet med å lære tolkbare maskinlæringsmodeller fra usikker og manglende informasjon. Vi utvikler først en ny dyplæringsarkitektur, RIDDLE: Rule InDuction with Deep LEarning (regelinduksjon med dyp læring), basert på egenskapene til mulighetsteori. Med eksperimentelle resultater og sammenligning med FURIA, en eksisterende moderne metode for regelinduksjon, er RIDDLE en lovende regelinduksjonsalgoritme for å finne regler fra data. Deretter undersøker vi læringsoppgaven formelt ved å identifisere regler med konfidensgrad knyttet til dem i exact learning-modellen. Vi definerer formelt teoretiske rammer og viser forhold som må holde for å garantere at en læringsalgoritme vil identifisere reglene som holder i et domene. Til slutt utvikler vi en algoritme som lærer regler med tilhørende konfidensverdier i exact learning-modellen. Vi foreslår også en teknikk for å simulere spørringer i exact learning-modellen fra data. Eksperimenter viser oppmuntrende resultater for å lære et sett med regler som tilnærmer reglene som er kodet i data.We address the problem of learning interpretable machine learning models from uncertain and missing information. We first develop a novel deep learning architecture, named RIDDLE (Rule InDuction with Deep LEarning), based on properties of possibility theory. With experimental results and comparison with FURIA, a state of the art method, RIDDLE is a promising rule induction algorithm for finding rules from data. We then formally investigate the learning task of identifying rules with confidence degree associated to them in the exact learning model. We formally define theoretical frameworks and show conditions that must hold to guarantee that a learning algorithm will identify the rules that hold in a domain. Finally, we develop an algorithm that learns rules with associated confidence values in the exact learning model. We also propose a technique to simulate queries in the exact learning model from data. Experiments show encouraging results to learn a set of rules that approximate rules encoded in data.Doktorgradsavhandlin

Representing archaeological uncertainty in cultural informatics

This thesis sets out to explore, describe, quantify, and visualise uncertainty in a cultural informatics context, with a focus on archaeological reconstructions. For quite some time, archaeologists and heritage experts have been criticising the often toorealistic appearance of three-dimensional reconstructions. They have been highlighting one of the unique features of archaeology: the information we have on our heritage will always be incomplete. This incompleteness should be reflected in digitised reconstructions of the past. This criticism is the driving force behind this thesis. The research examines archaeological theory and inferential process and provides insight into computer visualisation. It describes how these two areas, of archaeology and computer graphics, have formed a useful, but often tumultuous, relationship through the years. By examining the uncertainty background of disciplines such as GIS, medicine, and law, the thesis postulates that archaeological visualisation, in order to mature, must move towards archaeological knowledge visualisation. Three sequential areas are proposed through this thesis for the initial exploration of archaeological uncertainty: identification, quantification and modelling. The main contributions of the thesis lie in those three areas. Firstly, through the innovative design, distribution, and analysis of a questionnaire, the thesis identifies the importance of uncertainty in archaeological interpretation and discovers potential preferences among different evidence types. Secondly, the thesis uniquely analyses and evaluates, in relation to archaeological uncertainty, three different belief quantification models. The varying ways that these mathematical models work, are also evaluated through simulated experiments. Comparison of results indicates significant convergence between the models. Thirdly, a novel approach to archaeological uncertainty and evidence conflict visualisation is presented, influenced by information visualisation schemes. Lastly, suggestions for future semantic extensions to this research are presented through the design and development of new plugins to a search engine

Belief Change in Reasoning Agents: Axiomatizations, Semantics and Computations

The capability of changing beliefs upon new information in a rational and efficient way is crucial for an intelligent agent. Belief change therefore is one of the central research fields in Artificial Intelligence (AI) for over two decades. In the AI literature, two different kinds of belief change operations have been intensively investigated: belief update, which deal with situations where the new information describes changes of the world; and belief revision, which assumes the world is static. As another important research area in AI, reasoning about actions mainly studies the problem of representing and reasoning about effects of actions. These two research fields are closely related and apply a common underlying principle, that is, an agent should change its beliefs (knowledge) as little as possible whenever an adjustment is necessary. This lays down the possibility of reusing the ideas and results of one field in the other, and vice verse. This thesis aims to develop a general framework and devise computational models that are applicable in reasoning about actions. Firstly, I shall propose a new framework for iterated belief revision by introducing a new postulate to the existing AGM/DP postulates, which provides general criteria for the design of iterated revision operators. Secondly, based on the new framework, a concrete iterated revision operator is devised. The semantic model of the operator gives nice intuitions and helps to show its satisfiability of desirable postulates. I also show that the computational model of the operator is almost optimal in time and space-complexity. In order to deal with the belief change problem in multi-agent systems, I introduce a concept of mutual belief revision which is concerned with information exchange among agents. A concrete mutual revision operator is devised by generalizing the iterated revision operator. Likewise, a semantic model is used to show the intuition and many nice properties of the mutual revision operator, and the complexity of its computational model is formally analyzed. Finally, I present a belief update operator, which takes into account two important problems of reasoning about action, i.e., disjunctive updates and domain constraints. Again, the updated operator is presented with both a semantic model and a computational model