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Remote control and home automation terms are becoming very important nowadays. Sensors are everywhere: HVAC (Heating, Ventilation and Air Conditioning) systems, fridges, security cameras, hard disks inside servers, voltages, temperatures and fan speeds in modern computers are widespread. Access to these sensors, both physical and virtual (e.g. data retrieved from an operating system), is possible thanks to third party tools and applications. This work outlines a framework called OBRECAD (Object Based REmote Control And Domotics). OBRECAD allows the communication between sensors, generic devices and actuators. It is powerful enough to support current and future sensor implementations and usages thanks to the realized hardware abstraction, yet easy enough to use. It can be easily implemented even on a micro-controller. The command oriented messaging framework allow an ubiquitous access in a very easy way using even text only email, or any SMS capable device. Complex architectures can also be managed in an hierarchical way thanks to the gateway component. Finally, a Java prototype was developed, but any programming language can be used. The prototype is configured using XML files that describe the connections between devices.

In this paper we propose an approach to optimization of web marketing content based on online discrete particle swarm optimization (PSO) model. The idea behind online PSO is to evaluate the collective user feedback as the PSO objective function which drive particles velocities in the hybrid continuous-discrete space of web content features. The PSO coordinate the process of sampling collective user behavior in order to optimize the web marketing metric. Experiments in the online banner optimization scenario show that the method converges faster than other methods and avoid some common drawbacks such as local optimal and hybrid discrete/continuous features management. The proposed online optimization method is general and can be applied to other web marketing or business intelligent contexts.

A framework for online user behavior soft modeling is presented in this work. Behavior models of users in dynamic virtual environments has been described in the literature in terms of timed transition automata which can be compiled in a planning domain. The extended notion of soft timed transition automata is proposed in order to recognize a larger class of user histories. The notion of deviation from the user model allows to assess and evaluate in real time the dynamic behavior of users acting in virtual environments, such as e-learning and e-business platforms. The timed automata model allows to describe virtually infinite sequences of user actions subject to temporal constraints, while soft measures allows to assess recognition of behaviors by evaluating the amount of temporal deviation, additional or omitted actions contained in an observed behavior. The proposed model allow the partial recognition of user history also when the observed actions only partially meets the given behavior model constraints. This approach is more realistic for real time user support systems, with respect to standard boolean model recognition, when more than one user model is potentially available the amount of deviation from the models can be used as guide to generate the system support by anticipation, projection and other known techniques. Experiments based on logs from an e-learning platforms and plan compilation of the soft timed automaton show the expressivity of the proposed model.

A model of user behaviour based automated planning is introduced in this work. The behaviour of users of web interactive systems can be described in term of a planning domain encapsulating the timed actions patterns representing the intended user profile. The user behaviour recognition is then posed as a planning problem where the goal is to parse a given sequence of user logs of the observed activities while reaching a final state. A general technique for transforming a timed finite state automata description of the behaviour into a numerical parameter planning model is introduced. Experimental results show that the performance of a planning based behaviour model is effective and scalable for real world applications. A major advantage of the planning based approach is to represent in a single automated reasoning framework problems of plan recognitions, plan synthesis and plan optimisation., {"references":["Alur R., Dill D., \"A theory of timed automata\", Theoretical Computer\nScience, vol. 126, num. 2, p. 183-235, 1994.","B. Berendt, M. Spiliopoulou, \"Analysis of navigation behaviour in web\nsites integrating multiple information systems\", The VLDB Journal, 9,\nSpringer-Verlag, 2000, pp. 56-75.","B. Berendt, G. Stumme, A. Hotho, \"Usage mining for and on the\nSemantic Web\", In: H. Kargupta, A. Joshi, K. Sivakumar, & Y. Yesha\n(Eds.), Data Mining: Next Generation Challenges and Future\nDirections, AAAI/MIT Press, Menlo Park, CA, 2004, pp. 461-480.","S. Ceri, F. Daniel, V. Demaldé, F. M. Facca, \"An Approach to User-\nBehavior-Aware Web Applications\", ICWE 5 Proceedings, Sydney,\nAustralia, Springer, 2005.","R. Cooley, B. Mobasher, J. Srivastava, \"Data preparation for mining\nworld wide web browsing patterns\", Journal of Knowledge and\nInformation Systems, 1(1), 1999.","F. Masseglia, P. Poncelet, M. Teisseire, A. Marascu, \"Web Usage\nMining: Extracting Unexpected Periods from Web Logs\", TDM 2 -\nICDM'05 Proceedings, Houston, USA, 2005.","M. M├╝hlenbrock, \"Automatic Action Analysis in an Interactive Learning\nEnvironment\", AIED-2005 Proceedings, Amsterdam, NL, pp. 73-80.","M. Teltzrow, B. Berendt, \"Web-Usage-Based Success Metrics for Multi-\nChannel Businesses\", WebKDD 2003 9th ACM SIGKDD Proceedings,\nWashington DC, USA, 2003.","Marco Baioletti, Stefano Marcugini, Alfredo Milani: Encoding Planning\nConstraints into Partial Order Planners. KR98 Proceeding, 6t Int. Conf.\non Principles of Knowledge Representation and Reasoning, pp.608-616,\nMorgan Kauffmann 1998, ISBN 1-55860-554-1.\n[10] Marco Baioletti Stefano Marcugini, Alfredo Milani: Task Planning and\nPartial Order Planning: A Domain Transformation Approach. in Lecture\nNotes in Computer Science, Vol.1348, pp.52-63, Springer-Verlag,\nBerlin, Germany, 1997, ISBN 3-540-64912-8.\n[11] Blum, A., and Frust, M. Fast planning graph analysis. Artificial\nIntelligence 90, 1-2 (1997), 279-298.\n[12] Kautz, H., and Selman, B. Planning as satisfiability. In 10th European\nConfernce on Artificial Intelligence (ECAI) (1992), B. Neumann, Ed.,\nWiley & Sons, pp. 360-363.\n[13] Kautz, H., and Selman, B. BLACKBOX: A new approach to the\napplication of thorem proving to ploblem solving. In working notes of\nthe AIPS-98 Workshop on Planning as Combinatorial Search (1998),\npp. 58-60.\n[14] Suriani, S. Numerical Parameters in Automated Planning. PhD Thesis -\nDepartment of Mathematics and Computer Science - University of\nPerugia.\n[15] Wolfaman, S., and Weld, D. The LPSAT engine and its application to\nresource planning. In Proc. of IJCAI-99 (!999).\n[16] Vossen, T., Ball, M. Lotem, A. and Nau, D. Applying integer\nprogramming to AI planning, Knowledge Engineering Review 16:85-\n100, 2001.\n[17] Van de Briel, M. and Kambhampati, S. Optiplan: Unifying ip-based and\ngraph-based planning. Journal of Artificial Intelligence Research 24\n(2005), 919-931."]}

A framework for online user behavior soft modeling is presented in this work. Behavior models of users in dynamic virtual environments has been described in the literature in terms of timed transition automata which can be compiled in a planning domain. The extended notion of soft timed transition automata is proposed in order to recognize a larger class of user histories. The notion of deviation from the user model allows to assess and evaluate in real time the dynamic behavior of users acting in virtual environments, such as e-learning and e-business platforms. The timed automata model allows to describe virtually infinite sequences of user actions subject to temporal constraints, while soft measures allows to assess recognition of behaviors by evaluating the amount of temporal deviation, additional or omitted actions contained in an observed behavior. The proposed model allow the partial recognition of user history also when the observed actions only partially meets the given behavior model constraints. This approach is more realistic for real time user support systems, with respect to standard boolean model recognition, when more than one user model is potentially available the amount of deviation from the models can be used as guide to generate the system support by anticipation, projection and other known techniques. Experiments based on logs from an e-learning platforms and plan compilation of the soft timed automaton shows the expressivity of the proposed model.

An evolutionary adaptive algorithm for solving a class of online service provider problems in a dynamical web environment is introduced. In the online service provider scenario, a system continuously generates digital products and service instances by assembling components (e.g. headlines of online newspapers, search engine query results, advertising lists) to fulfill the requirements of a market of anonymous customers. The evaluation of a service instance can only be known by the feedback obtained after delivering it to the customer over the internet or through telephone networks. In dynamic domains available components and customer/agents preferences are changing over the time. The proposed algorithm employs typical genetic operators in order to optimize the service delivered and to adapt it to the environment feedback and evolution. Differently from classical genetic algorithms the goal of such systems is to maximize the average fitness instead of determining the single best optimal service/product. Experimental results for different classes of services, online newspapers and search engines, confirm the adaptive behavior of the proposed technique.

The blind image restoration problem consists in estimating the original image from blurry and noisy data, without knowing the involved blur operator. The problem is well known to be ill-posed even in the not-blind formulation, nevertheless the use of regularization techniques allows to define the solution of the problem as the minimum of an energy function. In this paper we solve the blind restoration problem with a evolutionary approach. A population of blur operators is evolved with a fitness given by the opposite of the energy function to be minimized. Since the fitness evaluation, calculated on the whole image, represents a significant computational overhead which can make the method unfeasible for large images, an original technique of dynamical local fitness evaluation has been designed and integrated in the evolutionary scheme. The subimage evaluation area is dynamically changed during evolution of the population. The underlying hypothesis is that the explored subareas are significatively representative of the features of blurs and noises in the global image. The experimental results confirm the adequacy of such a method: in some cases the proposed genetic blind reconstruction finds qualitatively better solutions outperforming the not-blind standard deterministic algorithm.

Adaptive search engines (ASE), used in the retrieval of multimedia objects adapt their behavior depending on the user feedback in order to eventually converge to the optimal answer. The adaptive architecture has been shown to improve the performance in case of multimedia objects retrieval, when pre-indexing techniques are costly or can be applied only partially. The continuous user feedbacks on the lists of returned objects are used to filter out irrelevant objects and promote the relevant ones. This work propose an original dealer/opponent game model for ASE. The system/user interactive process which takes place in ASE can be modeled as a discovery game between a dealer, the user community which holds a secret consisting in the optimal answer to a query, and an opponent, i.e. the system, which tries to discover the secret by submitting tentative solutions on which it receives the user/dealer feedback. It is shown how the complexity of the game can be related to known games. An evolutionary approach to solve the ASE game is also presented. Experimental results shows convergence to the optimal solution with acceptable performance for real domain size. The proposed schema is quite general and can fit other adaptive search architectures which appear in e-business and e-commerce applications.

In the radically changing framework of education and training, the E-studium project (http://e-studium.unipg.it) developed at the University of Perugia, proposes a dynamic and versatile education system, managing effectively the learning process and using new ICT technologies to respond individually to the needs of a wide audience of students disseminated in the country. The organization model is an integrated e-learning system, known as blended e-learning (realized through the course management system Moodle), aiming at a renewal of academic education with a focus on quality of teaching, on support of in-class teaching by on-line services and materials, allowing better use and participation by both full-time and part-time students. Beside students, also academic teachers work as experimental subjects for the technologies implemented. In this work the "E-studium" project is presented showing the different phases of development, implementation and analysis of results including usage.

The traditional methods and strategies for educational- learning training in universities do not respond to the learning needs of students applying to "special training courses" (Corsi Abilitanti speciali) that are already experienced teachers and workers, and that for various reasons and motivations are not able to attend classroom courses. In this environment, SSIS Umbria (post-graduate training for high school teaching qualification) is experimenting a form of blended e-learning aimed at supporting classroom lessons, during which a network approach to knowledge and some forms of collaboration and cooperation in know-how building are integrated. Experimentation has proposed integration of on-line activities that students are able to carry out on their own, and other activities to be performed in small learning groups, consistent with the class courses envisaged for teacher training. The experimentation has also allowed to assess, through log analysis, the degree of interest of participants and distribution of their time between the various activities envisaged by the e-learning software. The method for evaluation of participant activities has not been based on a simple recording of the on-line connection time, but rather on the assignation of a previously-set time "token" in minutes to each task performed, according to the commitment forecast for the accomplishment of each single activity. This type of monitoring has been organized by backing up software time-counting of "minute-amounts" coming from on-line practice with tutor control, in order to supervise the respect of an accurate netiquette.

In the radically changing framework of education and training, the E-studium project (http://e-studium.unipg.it) developed at the University of Perugia, proposes a dynamic and versatile education system, managing effectively the learning process and using new ICT technologies to respond individually to the needs of a wide audience of students disseminated in the country. The organization model is an integrated e-learning system, known as blended e-learning (realized through the course management system Moodle), aiming at a renewal of academic education with a focus on quality of teaching, on support of in-class teaching by on-line services and materials, allowing better use and participation by both full-time and part-time students. Beside students, also academic teachers work as experimental subjects for the technologies implemented. In this work the "E-studium" project is presented showing the different phases of development, implementation and analysis of results including usage.

This work introduces a framework for a representation of web-pages by means of multiple knowledge structures in order to improve semantic usage modelling by multiple semantic information. The proposed approach generalizes existing models, developed mainly for e-commerce systems, in order to include and integrate features of hypertext-structured educational systems such as e-learning systems.

This paper describes the ESIDE-Depian intrusion detection and prevention system, which uses Bayesian structural and parametric learning and also evidence propagation and adaptation, in order to improve the accuracy and manageability of network intrusion detection systems (NIDS). Current NIDS do not consider the two main detection paradigms, i.e. misuse detection and anomaly detection, in an unified style, so the analysis is not inherently complete. Besides, historical data are not generally used, neither for analysis nor for sequential adaptation of the knowledge representation models used for detection; hence this wealthy information about the essence and the potential trends of the target system is not commonly considered. Thus, by the generalized use of Bayesian belief networks, ESIDE-Depian achieves the main goal of detecting and preventing both well-known and also zero-day attacks with excellent results, by means of unified real-time analysis of network traffic.

The diffusion of domotic and ambient intelligence systems have introduced a new vision in which autonomous deliberative agents operate in environments where reactive responses of devices can be cooperatively exploited to fulfill the agent's goals. In this article a model for automated planning in reactive environments, based on numerical planning, is introduced. A planner system, based on mixed integer linear programming techniques, which implements the model, is also presented. The planner is able to reason about the dynamic features of the environment and to produce solution plans, which take into account reactive devices and their causal relations with agent's goals by exploitation and avoidance techniques, to reach a given goal state. The introduction of reactive domains in planning poses some issues concerning reasoning patterns which are briefly depicted. Experiments of planning in reactive domains are also discussed.

In this paper a planning system for goal directed integration of web services is presented. The presented model extends classical planning to manage some forms of non determinism in service execution and to manage collections of objects. The dynamical evolving domain of available web services require flexible tools for composing the given resources in order to fulfil user goals which are also unpredictable. Automated planning techniques can be efficiently used to realise dynamical and adaptive web service composition. The domain of available services is represented by a set of planning operators, the behaviour of the composed service is described by a final goal and the generated solution plans are used for synthesis of web service scripts.

In this work we introduce an adaptive architecture for supporting news information services in a dynamical web environment. In the news provider problem, a system continuously generates service instances (e.g.the news of a online newspaper) which should meet the requirements of a market of readers/agents which are supposed unknown. The only way for the system to know the evaluation of a service instance, i.e. a newspaper instance, is the feedback obtained after delivering it to the reader. In a dynamical environment the domain of the service offered is changing and the readers/agents are changing their preferences over the time. The proposed algorithm for newpapers production employs typical genetic operators in order to optimize the product delivered and to adapt it to the environment feedback and domain evolution. Experiments shows the capability of dynamical adaptation of the algorithm..

In this paper a system for goal directed integration of web services based on automated planning is presented. The increasing number of web services available on the net poses the problem of having efficient tools in order to integrate existing services for obtaining complex services which reflect user goals and needs. In this scenario, automated planning techniques represent promising components of such dynamical and evolutionary systems. In the proposed architectural model, web services and user goals are modeled as planning operators and goals, while the generated solution plans are used for directly generating web service scripts. An extended planning model based on the notion of output variable has been introduced in order to take into account of results produced by services invocations. A technique called semantic wrapper has been developed for modeling services as operators. In addition to a simple automation tasks, web services plans also optimise's the use of resources, and they provides useful information on variables bindings and the parallel structure of web services calls. The implementation of P4WS, a planner with output variables which demonstrated the model is described and experimental results are presented.

This paper present an architecture based on evolutionary 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 to model 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.

This paper shows how current planning technology can be used in order to model online educational activity. In particular the automated planning approach allows to model the interactive process of designing the structure of educational tools and entities, such as courses, curricula studiorum, examples and training exercises, handbooks. Planning models allow to exploit educational knowledge bases in order to produce flexible and adaptive tools which are suited on the individual educational needs of the users. In addition to the generation of educational support material, state-of-the-art planning systems can also be used to monitor the interactive learning task of the user. Recent advancements on planning models based on resources management allow to refine and tune user modelling goals in order to realize a personalized service. An example of educational domain model based on planning with resources, is shown, in order to support the dynamical generation and monitoring of web based online courses.

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 maximizing 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 behavior 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 to model highly evolutionary domains where both services providers and clients change and evolve over time. The flexibility and the adaptive behavior of this approach seems to be very relevant and promising for applications characterized 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 characterized by a massive number of anonymous clients/users which require personalized services, such as in the case of many new IT applications.

In this paper the classification of the (n, 3)-arcs in PG(2, 7) is presented. It has been obtained using a computer-based exhaustive search that exploits projective equivalence and produces exactly one representative of each equivalence class. For each (n, 3)-arc, the automorphism group and the maximal size of a contained k-arc have been found.

Most advertisement systems widely used in Internet try to improve advertisement process by targeting specific groups of potential customers. Many systems exploit the information directly provided by the user and the data collected by monitoring user activities in order to built accurate user profiles, which determines the success of the advertisement process.

In this paper we present RDPPlan, an automated problem solver which generates plans of actions in order to satisfy logical goals and numerical goals on uncertain resources. Planning with resources is a component of many applications which range from robot planning to automated manufacturing and automatic software composition. In the classical planning model the actions describe purely logical state transitions; some extensions have been recently proposed in order to manage numerical resources which can be produced/consumed in exact amounts. Unfortunately in real domains it is impossible to make accurate and exact previsions about resource production/consumption because of the inherent uncertainty of real world. The planning model introduced in RDPPlan allows to manage uncertainty about the initial value of resources and actions that can make uncertain updates of numerical resources. The proposed model uses the notion of trapezoidal fuzzy intervals to handle the uncertainty on resource values; the solving algorithm extends, for fuzzy resources, the propagation rules of the planner DPPlan.

In this paper a model for planning with fuzzy resources is presented. Nearly all existing models of planning with resources need to work with exact value of the resources modified by actions execution. Therefore these models cannot deal with more realistic situations in which the resources quantities are not completely known. The model presented here allows to manage domains more tailored to real world, where preconditions and effects over resources can be specified in terms of uncertain intervals using fuzzy sets. Moreover it is possible to define problems with mixed logical/quantitative and pure numerical goals. The fuzzy approach adequately represents the kind of uncertainty existing in planning domains. The choice of trapezoidal fuzzy intervals represents a solution which combine a simple, but expressive representation, with computational simplicity. It is also presented a search strategy for goals over resources that provides a specific method to guide search. This strategy is necessary when solving “pure numerical problems”. Finally some experimental results are presented and commented.

A method is introduced which generates plan object from plans. A plan object is a description of a plan which can be used and composed in the planning process without regard of details of its internal decomposition. Plan objects induce a hierarchy between actions. The method is based on a planning model which is an extension of the classical Strips-like state based models, with additional clauses in order to generate and represent nonlinear and parallel plans. Transforming plans in plan objects produces new actions descriptions, which have the same form of elementary actions and which can be managed by the same mechanisms of plan synchronization, conflict analysis and plan synthesis of usual actions descriptions.

This paper introduces Community Adaptive Search Engines (CASE) for multimedia object retrieval. CASE systems adapt their behaviour depending on the collective feedback of the users in order to eventually converge to the optimal answer. The community adaptive approach uses continuous user feedbacks on the lists of returned objects in order to filter out irrelevant objects and promote the relevant ones. An original dealer/opponent game model for CASE is proposed and an evolutionary approach to solve the CASE game is also presented. Experimental results shows convergence to the optimal solution with acceptable performance for real domain sizes.

In the changing framework of education and training, the e-studium project proposes a dynamic and versatile education system, effectively managing the learning process and using Information and Communication Technologies (ICTs) to respond to the specific characteristics of a variety of different degree programmes and courses and, at the same time, to the individual needs of a wide audience of students disseminated throughout Italy. The organisation model adopted in e-studium is an integrated multiple instances e-learning system. At the kernel of e-studium is MetaLearn, a software integration component which connects the different information and authentication sources, such as the academic registrar (information about students) and faculties databases (data on lecturers and programmes) with the target multiple e-learning platforms. In the experiment the MetaLearn architecture has been implemented on top of a set of target Moodle Course Management System (CMS) instances, thus realising the blended e-learning approach. In this work the main architectural features of MetaLearn and the different phases of development and implementation of the e-studium project are presented. The results also include usage analysis and a discussion on the impact of the quality of teaching on students, and on the requirements posed on the academic members who participated in the project.

In this work a model for planning with multivalued fluents and graded actions, based on the infinity-valued Lukasiewicz logic, is introduced. In multivalued planning, fluents and actions can assume truth values in [0, 1]. Multivalued fluents and graded actions allow to model many real situations where some features of the world cannot be modelled with boolean values and where actions can be executed with varying strength which produces graded effects as well. A correct/complete algorithm which solves bounded multivalued planning problems based on MIP compilation is also described and a prototype implementation is presented.

A reason maintenance system which extends an ATMS through Mukaidono's fuzzy logic is described. It supports a problem solver in situations affected by incomplete information and vague data, by allowing nonmonotonic inferences and the revision of previous conclusions when contradictions are detected.

A reason maintenance system which extends an ATMS through Mukaidono's fuzzy logic is described. It supports a problem solver in situations affected by incomplete information and vague data, by allowing nonmonotonic inferences and the revision of previous conclusions when contradictions are detected.

The notion of Affected and Protected clause extends classical action models to enable a more reliable composition of parallel nonatomic actions. A notion of legal action description is given. On the basis of the given model conditions are stated in order to allow analysis of conflicts in parallel and nonlinear planning.

In this report we describe the hate speech detection system for the Italian language developed by a joint team of researchers from the two universities of Perugia (University for Foreigners of Perugia and University of Perugia). The experimental results obtained in the HaSpeeDe task of the Evalita 2018 evaluation campaign are analyzed. Finally, a suggestion for future research directions is provided in the conclusion. In questo documento descriviamo il sistema di hate speech detection per la lingua Italiana sviluppato da una squadra di ricercatori dell’Università per Stranieri di Perugia e dell’Università degli Studi di Perugia. I risultati sperimentali ottenuti nel task HaSpeeDe, organizzato nell’ambito di Evalita 2018, sono riportati e analizzati. Infine, una possibile direzione di ricerca è fornita nelle conclusioni.

In this paper a new discrete Differential Evolution algorithm for the Permutation Flowshop Scheduling Problem with the total flowtime criterion is proposed. The core of the algorithm is the distance-based differential mutation operator defined by means of a new randomized bubble sort algorithm. This mutation scheme allows the Differential Evolution to directly navigate the permutations search space. Experiments were held on a well known benchmark suite and the results show that our proposal outperforms state-of-the-art algorithms on the majority of the problems.

In this paper a planning framework based on Ant Colony Optimization techniques is presented. It is well known that finding optimal solutions to planning problems is a very hard computational problem. Stochastic methods do not guarantee either optimality or completeness, but it has been proved that in many applications they are able to find very good, often optimal, solutions. We propose several approaches based both on backward and forward search over the state space, using several heuristics and testing different pheromone models in order to solve sequential optimization planning problems.

This paper presents theoretical results and techniques for representing and managing task network goals in the framework of partial order planning. Task oriented formalisms are more expressive than partial order based formalism for problem goals in dynamical and changing domains, but they are not more powerful. We prove that it is always possible to express a task network problem in terms of an equivalent problem stated in partial order planning formalism. The task network model has been extended to describe external events (EETN), a feature not present in many planning models. The equivalence between this new model and PO formalism is also proved.

The gray levels quantization technique is used to generate images which limit the number of color levels resulting in a reduction of the image size, while it preserves the quality perceived by human observers. The problem is very relevant for image storage and web distribution, as well as in the case of devices with limited bandwidth, storage and/or computational capabilities. An efficient evolutionary algorithm for the inverse gray level quantization problem, based on a technique of dynamical local fitness evaluation, is presented. A population of blur operators is evolved with a fitness given by the energy function to be minimized. In order to avoid the unfeasible computational overhead due to the fitness evaluation calculated on the entire image, an innovative technique of dynamical local fitness evaluation has been designed and integrated in the evolutionary scheme. The sub-image evaluation area is dynamically changed during evolution of the population, and the evolutionary scheme operates a form of machine learning while exploring subarea which are significatively representative of the global image. The experimental results confirm the adequacy of such a method.

A planning problem can be entirely encoded as a set of linear temporal logic (LTL) formulae, in such a way that planning is reduced to model search. In order for this approach to be effective, it is important to enhance the performances of LTL provers. In this work, we study a parallel algorithm for LTL model search, based on the tableaux calculus. In paritcular, the approach presented here is based on the “divide et impera” approach: a task in tableaux construction is identified that can be split into smaller homogeneous processes. The parallelization acts during the construction of each time state: the set of formulas to be expanded is split into k disjoint subsets (where k is the number of processes), the k tableaux expansions are carried out in parallel, and the k results are suitably combined. First promising experimental results are also presented: they are based on the algorithm implementation on a cluster of non homogeneous machines.