181 research outputs found
Soft behaviour modelling of user communities
A soft modelling approach for describing behaviour in on-line user communities is introduced in this work. Behaviour models of individual users in dynamic virtual environments have been described in the literature in terms of timed transition automata; they have various drawbacks. Soft multi/agent behaviour automata are defined and proposed to describe multiple user behaviours and to recognise larger classes of user group histories, such as group histories which contain unexpected behaviours. The notion of deviation from the user community model allows defining a soft parsing process which assesses and evaluates the dynamic behaviour of a group of users interacting in virtual environments, such as e-learning and e-business platforms. The soft automaton model can describe virtually infinite sequences of actions due to multiple users and subject to temporal constraints. Soft measures assess a form of distance of observed behaviours by evaluating the amount of temporal deviation, additional or omitted actions contained in an observed history as well as actions performed by unexpected users. The proposed model allows the soft recognition of user group histories also when the observed actions only partially meet the given behaviour model constraints. This approach is more realistic for real-time user community support systems, concerning standard boolean model recognition, when more than one user model is potentially available, and the extent of deviation from community behaviour models can be used as a guide to generate the system support by anticipation, projection and other known techniques. Experiments based on logs from an e-learning platform and plan compilation of the soft multi-agent behaviour automaton show the expressiveness of the proposed model
An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis
open access articleThis article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions
Online Genetic Algorithms
This paper present a technique based on genetic algorithms for generating online adaptive services.
Online adaptive systems provide flexible services to a mass of clients/users for maximising some system goals,
they dynamically adapt the form and the content of the issued services while the population of clients evolve
over time.
The idea of online genetic algorithms (online GAs) is to use the online clients response behaviour as a fitness
function in order to produce the next generation of services. The principle implemented in online GAs, “the
application environment is the fitness”, allow modelling highly evolutionary domains where both services
providers and clients change and evolve over time.
The flexibility and the adaptive behaviour of this approach seems to be very relevant and promising for
applications characterised by highly dynamical features such as in the web domain (online newspapers, e-
markets, websites and advertising engines). Nevertheless the proposed technique has a more general aim for
application environments characterised by a massive number of anonymous clients/users which require
personalised services, such as in the case of many new IT applications
A Two Layered Model for Evolving Web Resources
In this paper the key features of a two-layered model for describing the semantic of dynamical web
resources are introduced.
In the current Semantic Web proposal [Berners-Lee et al., 2001] web resources are classified into static
ontologies which describes the semantic network of their inter-relationships [Kalianpur, 2001][Handschuh &
Staab, 2002] and complex constraints described by logical quantified formula [Boley et al., 2001][McGuinnes &
van Harmelen, 2004][McGuinnes et al., 2004], the basic idea is that software agents can use techniques of
automatic reasoning in order to relate resources and to support sophisticated web application.
On the other hand, web resources are also characterized by their dynamical aspects, which are not adequately
addressed by current web models.
Resources on the web are dynamical since, in the minimal case, they can appear or disappear from the web and
their content is upgraded. In addition, resources can traverse different states, which characterized the resource
life-cycle, each resource state corresponding to different possible uses of the resource. Finally most resources
are timed, i.e. they information they provide make sense only if contextualised with respect to time, and their
validity and accuracy is greatly bounded by time.
Temporal projection and deduction based on dynamical and time constraints of the resources can be made and
exploited by software agents [Hendler, 2001] in order to make previsions about the availability and the state of a
resource, for deciding when consulting the resource itself or in order to deliberately induce a resource state
change for reaching some agent goal, such as in the automated planning framework [Fikes & Nilsson,
1971][Bacchus & Kabanza,1998]
Timed Transition Automata as Numerical Planning Domain
A general technique for transforming a timed finite state automaton into an equivalent automated
planning domain based on a numerical parameter model is introduced. Timed transition automata have many
applications in control systems and agents models; they are used to describe sequential processes, where
actions are labelling by automaton transitions subject to temporal constraints. The language of timed words
accepted by a timed automaton, the possible sequences of system or agent behaviour, can be described in term
of an appropriate planning domain encapsulating the timed actions patterns and constraints. The time words
recognition problem is then posed as a planning problem where the goal is to reach a final state by a sequence of
actions, which corresponds to the timed symbols labeling the automaton transitions. The transformation is proved
to be correct and complete and it is space/time linear on the automaton size. Experimental results shows that the
performance of the planning domain obtained by transformation is scalable for real world applications. A major
advantage of the planning based approach, beside of the solving the parsing problem, is to represent in a single
automated reasoning framework problems of plan recognitions, plan synthesis and plan optimisation
Planning Technologies for the Web Environment: Perspectives and Research Issues
This work will explore and motivate perspectives and research issues related with the applications
of automated planning technologies in order to support innovative web applications. The target for the
technology transfer, i.e. the web, and, in a broader sense, the new Information Technologies (IT) is one of the
most changing, evolving and hottest areas of current computer science. Nevertheless many sub-area in this
field could have potential benefits from Planning and Scheduling (P&S) technologies, and, in some cases,
technology transfer has already started. This paper will consider and explore a set of topics, guidelines and
objectives in order to implement the technology transfer a new challenges, requirements and research issues
for planning which emerge from the web and IT industry.
Sample scenarios will be depicted to clarify the potential applications and limits of current planning
technology. Finally we will point out some new P&S research challenge issues which are required to meet
more advanced applicative goals
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A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation
open access articleThis article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter changes
Managing Interval Resources in Automated Planning
In this paper RDPPLan, a model for planning with quantitative resources specified as numerical
intervals, is presented. Nearly all existing models of planning with resources require to specify exact values for
updating resources modified by actions execution. In other words these models cannot deal with more
realistic situations in which the resources quantities are not completely known but are bounded by intervals.
The RDPPlan model allow to manage domains more tailored to real world, where preconditions and effects
over quantitative resources can be specified by intervals of values, in addition mixed logical/quantitative and
pure numerical goals can be posed. RDPPlan is based on non directional search over a planning graph, like
DPPlan, from which it derives, it uses propagation rules which have been appropriately extended to the
management of resource intervals. The propagation rules extended with resources must verify invariant
properties over the planning graph which have been proven by the authors and guarantee the correctness of
the approach. An implementation of the RDPPlan model is described with search strategies specifically
developed for interval resources
Classification of linear codes exploiting an invariant
We consider the problem of computing the equivalence classes of a set of
linear codes. This problem arises when new codes are obtained extending
codes of lower dimension. We propose a technique that, exploiting an
invariant simple to compute, allows to reduce the computational complexity
of the classification process. Using this technique the [13,5,8]_7, the
[14,5,9]_8 and the [15,4,11]_9 codes have been classified. These
classifications enabled us to solve the packing problem for NMDS codes for
q=7,8,9. The same technique can be applied to the problem of the
classification of other structures
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