57 research outputs found
Workshop on Fuzzy Control Systems and Space Station Applications
The Workshop on Fuzzy Control Systems and Space Station Applications was held on 14-15 Nov. 1990. The workshop was co-sponsored by McDonnell Douglas Space Systems Company and NASA Ames Research Center. Proceedings of the workshop are presented
Monitoring Complex Processes to Verify System Conformance: A Declarative Rule-Based Framework
Over the last 60 years, computers and software have favoured incredible advancements in every field.
Nowadays, however, these systems are so complicated that it is difficult – if not challenging – to understand whether they meet some requirement or are able to show some desired behaviour or property.
This dissertation introduces a Just-In-Time (JIT) a posteriori approach to perform the conformance check to identify any deviation from the desired behaviour as soon as possible, and possibly apply some corrections.
The declarative framework that implements our approach – entirely developed on the promising open source forward-chaining Production Rule System (PRS) named Drools – consists of three components:
1. a monitoring module based on a novel, efficient implementation of Event Calculus (EC),
2. a general purpose hybrid reasoning module (the first of its genre) merging temporal, semantic, fuzzy and rule-based reasoning,
3. a logic formalism based on the concept of expectations introducing Event-Condition-Expectation rules (ECE-rules) to assess the global conformance of a system.
The framework is also accompanied by an optional module that provides Probabilistic Inductive Logic Programming (PILP).
By shifting the conformance check from after execution to just in time, this approach combines the advantages of many a posteriori and a priori methods proposed in literature.
Quite remarkably, if the corrective actions are explicitly given, the reactive nature of this methodology allows to reconcile any deviations from the desired behaviour as soon as it is detected. In conclusion, the proposed methodology brings some advancements to solve the problem of the conformance checking, helping to fill the gap between humans and the increasingly complex technology.Negli ultimi 60 anni, i computer e i programmi hanno favorito incredibili avanzamenti in ogni campo.
Oggigiorno, purtroppo, questi sistemi sono così complicati che è difficile – se non impossibile – capire se soddisfano qualche requisito o mostrano un comportamento o una proprietà desiderati.
Questa tesi introduce un approccio a posteriori Just-In-Time (JIT) per effettuare il controllo di conformità ed identificare appena possibile ogni deviazione dal comportamento desiderato, ed eventualmente applicare qualche correzione.
Il framework dichiarativo che implementa il nostro approccio – interamente sviluppato su una promettente piattaforma open source di Production Rule System (PRS) chiamata Drools – si compone di tre elementi:
1. un modulo per il monitoraggio basato su una nuova implementazione efficiente di Event Calculus (EC),
2. un modulo generale per il ragionamento ibrido (il primo del suo genere) che supporta ragionamento temporale, semantico, fuzzy e a regole,
3. un formalismo logico basato sul concetto di aspettativa che introduce le Event-Condition-Expectation rules (ECE-rules) per valutare la conformità globale di un sistema.
Il framework è anche accompagnato da un modulo opzionale che fornisce Probabilistic Inductive Logic Programming (PILP).
Spostando il controllo di conformità da dopo l’esecuzione ad appena in tempo, questo approccio combina i vantaggi di molti metodi a posteriori e a priori proposti in letteratura.
Si noti che, se le azioni correttive sono fornite esplicitamente, la natura reattiva di questo metodo consente di conciliare le deviazioni dal comportamento desiderato non appena questo viene rilevato.
In conclusione, la metodologia proposta introduce alcuni avanzamenti per risolvere il problema del controllo di conformità, contribuendo a colmare il divario tra l’uomo e la tecnologia, sempre più complessa
An anytime deduction heuristic for first order probabilistic logic
This thesis describes an anytime deduction heuristic to address the decision and optimization form of the First Order Probabilistic Logic problem which was revived by Nilsson in 1986. Reasoning under uncertainty is always an important issue for AI applications, e.g., expert systems, automated theorem-provers, etc. Among the proposed models and methods for dealing with uncertainty, some as, e.g., Nilsson's ones, are based on logic and probability. Nilsson revisited the early works of Boole (1854) and Hailperin (1976) and reformulated them in an AI framework. The decision form of the probabilistic logic problem, also known as PSAT, consists of finding, given a set of logical sentences together with their probability value to be true, whether the set of sentences and their probability value is consistent. In the optimization form, assuming that a system of probabilistic formulas is already consistent, the problem is: Given an additional sentence, find the tightest possible probability bounds such that the overall system remains consistent with that additional sentence. Solution schemes, both heuristic and exact, have been proposed within the propositional framework. Even though first order logic is more expressive than the propositional one, more works have been published in the propositional framework. The main objective of this thesis is to propose a solution scheme based on a heuristic approach, i.e., an anytime deduction technique, for the decision and optimization form of first order probabilistic logic problem. Jaumard et al. [33] proposed an anytime deduction algorithm for the propositional probabilistic logic which we extended to the first order context
Model of Learning Ability
The problem domain of the investigation presented in this dissertation is knowledge increase. In particular the research is concerned with the process of knowledge increase. The research problem formulated is formulated a posteriori: "Which factors determine the increase of personal knowledge that occurs by absorbing a particular new knowledge of an individual, who is a member of an organization, and how these factors work?" To explore and shed light on this problem a number of disciplinary boundaries were engaged and some models, tools, descriptions, etc. were borrowed from a number of related disciplines. These areas are briefly presented in the dissertation, restricting presentation to the relevant issues. There are three models developed for this thesis and they are subsequently integrated into a fourth model. First the 'Model of Learning Willingness' (MLW) is developed to consider personal and organizational value systems. For this model, new concepts have been created, to indicate the position of new knowledge in both personal and organizational value systems. Stable and the unstable states of the model are identified as well as how it is possible to pass from one state to another as result of an interaction between the two value systems by means of influencing each other. Applying a 'systems theory approach' on the cognitive psychology conception of knowledge, the impact of the characteristics of existing knowledge on the absorption of new knowledge is described. The developed model is called the 'Model of Learning Capability' (MLC). - This is the second model. It is also necessary to pay attention to the ability to acquire new knowledge; this is described by the 'Model of Attention' (MA) - the third model. This model is based on two main factors, namely cognitive and social conditions. These three models are thus integrated into fourth one, which is called the 'Model of Learning Ability' (MLA). For exploration/validation the model is wwwed with the Doctus Knowledge-Based Expert System, which was also the means of comparing the evolved hypotheses with the input from reality, namely observations and thought experiments. The first insight from the model is a better understanding of the process of 'knowledge increase'. The model can also be used to support choosing the right person to learn a particular piece of new knowledge, to identify the reason for someone not performing well with regards to learning and/or identifying a possible way of improving the process. Using the logic of the model experts can also be evaluated in the process of knowledge acquisition when building an expert system. Considering the achieved results some new problems emerge: It is not known what motivates the personal value system during the knowledge absorption; it is not known if the model can be extended to other forms of knowledge increase besides learning; it is not known how the social factors apart from love (i.e. power and money) affect the attention. Some new research ideas also evolved from this investigation, e.g. an attempt to model the knowledge using dimensions of understanding
A case-based reasoning approach to improve risk identification in construction projects
Risk management is an important process to enhance the understanding of the project so as to support decision making. Despite well established existing methods, the application of risk management in practice is frequently poor. The reasons for this are investigated as accuracy, complexity, time and cost involved and lack of knowledge sharing. Appropriate risk identification is fundamental for successful risk management. Well known risk identification methods require expert knowledge, hence risk identification depends on the involvement and the sophistication of experts. Subjective judgment and intuition usually from par1t of experts’ decision, and sharing and transferring this knowledge is restricted by the availability of experts. Further, psychological research has showed that people have limitations in coping with complex reasoning. In order to reduce subjectivity and enhance knowledge sharing, artificial intelligence techniques can be utilised. An intelligent system accumulates retrievable knowledge and reasoning in an impartial way so that a commonly acceptable solution can be achieved. Case-based reasoning enables learning from experience, which matches the manner that human experts catch and process information and knowledge in relation to project risks. A case-based risk identification model is developed to facilitate human experts making final decisions. This approach exploits the advantage of knowledge sharing, increasing confidence and efficiency in investment decisions, and enhancing communication among the project participants
- …