240,490 research outputs found
Multi-layered reasoning by means of conceptual fuzzy sets
The real world consists of a very large number of instances of events and continuous numeric values. On the other hand, people represent and process their knowledge in terms of abstracted concepts derived from generalization of these instances and numeric values. Logic based paradigms for knowledge representation use symbolic processing both for concept representation and inference. Their underlying assumption is that a concept can be defined precisely. However, as this assumption hardly holds for natural concepts, it follows that symbolic processing cannot deal with such concepts. Thus symbolic processing has essential problems from a practical point of view of applications in the real world. In contrast, fuzzy set theory can be viewed as a stronger and more practical notation than formal, logic based theories because it supports both symbolic processing and numeric processing, connecting the logic based world and the real world. In this paper, we propose multi-layered reasoning by using conceptual fuzzy sets (CFS). The general characteristics of CFS are discussed along with upper layer supervision and context dependent processing
Temporal Data Modeling and Reasoning for Information Systems
Temporal knowledge representation and reasoning is a major research field in Artificial
Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to
model and process time and calendar data is essential for many applications like appointment
scheduling, planning, Web services, temporal and active database systems, adaptive
Web applications, and mobile computing applications. This article aims at three complementary
goals. First, to provide with a general background in temporal data modeling
and reasoning approaches. Second, to serve as an orientation guide for further specific
reading. Third, to point to new application fields and research perspectives on temporal
knowledge representation and reasoning in the Web and Semantic Web
Indexing the Event Calculus with Kd-trees to Monitor Diabetes
Personal Health Systems (PHS) are mobile solutions tailored to monitoring
patients affected by chronic non communicable diseases. A patient affected by a
chronic disease can generate large amounts of events. Type 1 Diabetic patients
generate several glucose events per day, ranging from at least 6 events per day
(under normal monitoring) to 288 per day when wearing a continuous glucose
monitor (CGM) that samples the blood every 5 minutes for several days. This is
a large number of events to monitor for medical doctors, in particular when
considering that they may have to take decisions concerning adjusting the
treatment, which may impact the life of the patients for a long time. Given the
need to analyse such a large stream of data, doctors need a simple approach
towards physiological time series that allows them to promptly transfer their
knowledge into queries to identify interesting patterns in the data. Achieving
this with current technology is not an easy task, as on one hand it cannot be
expected that medical doctors have the technical knowledge to query databases
and on the other hand these time series include thousands of events, which
requires to re-think the way data is indexed. In order to tackle the knowledge
representation and efficiency problem, this contribution presents the kd-tree
cached event calculus (\ceckd) an event calculus extension for knowledge
engineering of temporal rules capable to handle many thousands events produced
by a diabetic patient. \ceckd\ is built as a support to a graphical interface
to represent monitoring rules for diabetes type 1. In addition, the paper
evaluates the \ceckd\ with respect to the cached event calculus (CEC) to show
how indexing events using kd-trees improves scalability with respect to the
current state of the art.Comment: 24 pages, preliminary results calculated on an implementation of
CECKD, precursor to Journal paper being submitted in 2017, with further
indexing and results possibilities, put here for reference and chronological
purposes to remember how the idea evolve
Using the event calculus for tracking the normative state of contracts
In this work, we have been principally concerned with the representation of contracts so that their normative state may be tracked in an automated fashion over their deployment lifetime. The normative state of a contract, at a particular time, is the aggregation of instances of normative relations that hold between contract parties at that time, plus the current values of contract variables. The effects of contract events on the normative state of a contract are specified using an XML formalisation of the Event Calculus, called ecXML. We use an example mail service agreement from the domain of web services to ground the discussion of our work. We give a characterisation of the agreement according to the normative concepts of: obligation, power and permission, and show how the ecXML representation may be used to track the state of the agreement, according to a narrative of contract events. We also give a description of a state tracking architecture, and a contract deployment tool, both of which have been implemented in the course of our work.
Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback
Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
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