28 results on '"Milano, Michela"'
Search Results
2. Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning
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Coppens, Youri, Steckelmacher, Denis, Jonker, C.M., Nowe, A.S.P., Heintz, Fredrik, Milano, Michela, O’Sullivan, Barry, Informatics and Applied Informatics, Faculty of Sciences and Bioengineering Sciences, Artificial Intelligence, Computational Modelling, Heintz, Fredrik, Milano, Michela, and O'Sullivan, Barry
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Source code ,Inductive rule learning ,Artificial neural network ,business.industry ,Process (engineering) ,Computer science ,Policy distillation ,Computer Science - Artificial Intelligence ,media_common.quotation_subject ,Modèles mathématiques d'aide à la décision ,Intelligence artificielle ,Reinforcement Learning ,Task (project management) ,Machine Learning (cs.LG) ,Artificial Intelligence (cs.AI) ,Action (philosophy) ,Explainable AI ,Reinforcement learning ,Artificial intelligence ,business ,Set (psychology) ,media_common ,Interpretability - Abstract
Today’s advanced Reinforcement Learning algorithms produce black-box policies, that are often difficult to interpret and trust for a person. We introduce a policy distilling algorithm, building on the CN2 rule mining algorithm, that distills the policy into a rule-based decision system. At the core of our approach is the fact that an RL process does not just learn a policy, a mapping from states to actions, but also produces extra meta-information, such as action values indicating the quality of alternative actions. This meta-information can indicate whether more than one action is near-optimal for a certain state. We extend CN2 to make it able to leverage knowledge about equally-good actions to distill the policy into fewer rules, increasing its interpretability by a person. Then, to ensure that the rules explain a valid, non-degenerate policy, we introduce a refinement algorithm that fine-tunes the rules to obtain good performance when executed in the environment. We demonstrate the applicability of our algorithm on the Mario AI benchmark, a complex task that requires modern reinforcement learning algorithms including neural networks. The explanations we produce capture the learned policy in only a few rules, that allow a person to understand what the black-box agent learned. Source code: https://gitlab.ai.vub.ac.be/yocoppen/svcn2., info:eu-repo/semantics/published
- Published
- 2021
3. Sustainable policy making: a strategic challenge for artificial intelligence
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Milano, Michela, O'Sullivan, Barry, and Gavanelli, Marco
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Artificial intelligence ,Artificial intelligence ,Business ,European Union -- Environmental policy - Abstract
Policy making is an extremely complex process occurring in changing environments and affecting the three pillars of sustainable development: society, economy and the environment. Each political decision in fact implies [...]
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- 2014
4. Interactive Natural Language Technology for Explainable Artificial Intelligence
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Alonso, Jose M., Barro, Senén, Bugarín, Alberto, van Deemter, Kees, Gardent, Claire, Gatt, Albert, Reiter, Ehud, Sierra, Carles, Theune, Mariët, Tintarev, N., Yano, Hitoshi, Budzynska, Katarzyna, Heintz, Fredrik, Milano, Michela, O’Sullivan, Barry, RS: FSE DACS, Dept. of Advanced Computing Sciences, Human Media Interaction, and University of Twente
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Focus (computing) ,Modality (human–computer interaction) ,Explainable Artificial Intelligence ,Computer science ,business.industry ,Multi-modal explanations ,Visualization ,Trustworthiness ,Argumentative conversational agents ,Leverage (negotiation) ,Human-centered modeling ,Artificial intelligence ,Human-machine persuasive interaction ,business ,Natural language ,Ai systems - Abstract
We have defined an interdisciplinary program for training a new generation of researchers who will be ready to leverage the use of artificial intelligence (ai)-based models and techniques even by non-expert users. The final goal is to make ai self-explaining and thus contribute to translating knowledge into products and services for economic and social benefit, with the support of explainable ai systems. Moreover, our focus is on the automatic generation of interactive explanations in natural language, the preferred modality among humans, with visualization as a complementary modality.keywordsexplainable artificial intelligencetrustworthinessmulti-modal explanationsargumentative conversational agentshuman-centered modelinghuman-machine persuasive interaction.
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- 2021
5. An ideal team is more than a team of ideal agents
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Kurtan, Can, Yolum, Pinar, Dastani, Mehdi, De Giacomo, Giuseppe, Catala, Alejandro, Dilkina, Bistra, Milano, Michela, Barro, Senén, Bugarín, Alberto, Lang, Jérôme, Sub Intelligent Systems, and Intelligent Systems
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Artificial Intelligence - Abstract
The problem of building a team to perform a complex task is often more than an optimal assignment of subtasks to agents based on individual performances. Subtasks may have subtle dependencies and relations that affect the overall performance of the formed team. This paper investigates the dependencies between subtasks and introduces some desired qualities of teams, such as preserving privacy or fairness. It proposes algorithms to analyze and build teams by taking into account the dependencies of assigned subtasks and agent performances. The performance of the algorithms are evaluated experimentally based on a multiagent system that is developed to answer complex queries. We show that by improving an initial team iteratively, the algorithm obtains teams with higher performance.
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- 2020
6. Solving mathematical puzzles: A deep reasoning challenge. Position Paper
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CHESANI, FEDERICO, MELLO, PAOLA, MILANO, MICHELA, F. Chesani, P. Mello, M. Milano, Chesani, F., Mello, P., and Milano M.
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Artificial intelligence ,Deep reasoning ,Mathematical puzzle - Abstract
This is the era of big-data: high-volume, high-velocity and high-variety information assets are being collected, demanding cost-effective information processing. Analytic techniques primarily based on statistical methods are showing astonishing results, but exhibit also limited reasoning capabilities. On the other end of the spectrum the era of big- reasoning is emerging with next-generation cognitive and autonomous end-To-end solvers. A problem description in terms of text and diagrams is given: problem solvers should automatically understand the problem, identify its components, devise a model, identify a solving technique and find a solution with no human intervention. We propose a challenge: To design and implement an end-To-end solver for mathematical puzzles able to compete with primary school students. Mathematical puzzles require mathematics to solve them, but also logic, intuition and imagination are essential ingredients, thus calling for an unprecedented integration of many different AI techniques.
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- 2017
7. Solving Mathematical Puzzles: A Challenging Competition for AI.
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Chesani, Federico, Mello, Paola, and Milano, Michela
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ARTIFICIAL intelligence ,ARTIFICIAL intelligence competitions ,PROBLEM solving ,PUZZLES ,DECISION making - Abstract
Recently, a number of noteworthy results have been achieved in various fields of artificial intelligence, and many aspects of the problem-solving process have received significant attention by the scientific community. In this context, the extraction of comprehensive knowledge suitable for problem solving and reasoning, from textual and pictorial problem descriptions, has been less investigated, but recognized as essential for autonomous thinking in artificial intelligence. In this work we present a challenge where methods and tools for deep understanding are strongly needed for enabling problem solving: we propose to solve mathematical puzzles by means of computers, starting from text and diagrams describing them, without any human intervention. We are aware that the proposed challenge is hard and difficult to solve nowadays (and in the foreseeable future), but even studying and solving only single parts of the proposed challenge would represent an important step forward for artificial intelligence. [ABSTRACT FROM AUTHOR]
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- 2017
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8. Dealing with Incomplete Knowledge on CLP(FD) Variable Domains.
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Gavanelli, Marco, Lamma, Evelina, Mello, Paola, and Milano, Michela
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COMPUTER algorithms ,PROGRAMMING languages ,COMPUTER programming ,ARTIFICIAL intelligence ,SELF-organizing systems ,NEURAL computers - Abstract
Constraint Logic Programming languages on Finite Domains, CLP(FD), provide a declarative framework for Artificial Intelligence problems. However, in many real life cases, domains are not known and must be acquired or computed. In systems that interact with the outer world, domain elements synthesize information on the environment, they are not all known at the beginning of the computation, and must be retrieved through an expensive acquisition process. In this article, we extend the CLP(FD) language by combining it with a new sort (called Incrementally specified Sets, I-Set). In the resulting language, CLP(FD + I-Set), FD variables can be defined on partially or fully unknown domains (I-Set). Domains can be linked each other through relations, and constraints can be imposed on them. We describe a propagation algorithm (called Known Arc Consistency (KAC)) based on known domain elements, and theoretically compare it with arc-consistency. The language can be implemented on top of different CLP systems, thus letting the user exploit different possible semantics for domains (e.g., lists, sets or streams). We state the specifications that the employed system should provide, and we show that two different CLP systems (Conjunto and (log)) can be effectively used. We provide motivating examples and describe promising applications. [ABSTRACT FROM AUTHOR]
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- 2005
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9. Integrated Offline and Online Decision Making under Uncertainty
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Allegra De Filippo, Michele Lombardi, Michela Milano, De Filippo, Allegra, Lombardi, Michele, and Milano, Michela
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Artificial Intelligence ,Computer science ,Offline Stochastic Optimization Online Stochastic Optimization Decision Making Optimization Under Uncertainty ,Online decision making ,Data science - Abstract
This paper considers multi-stage optimization problems under uncertainty that involve distinct offline and online phases. In particular it addresses the issue of integrating these phases to show how the two are often interrelated in real-world applications. Our methods are applicable under two (fairly general) conditions: 1) the uncertainty is exogenous; 2) it is possible to define a greedy heuristic for the online phase that can be modeled as a parametric convex optimization problem. We start with a baseline composed by a two-stage offline approach paired with the online greedy heuristic. We then propose multiple methods to tighten the offline/online integration, leading to significant quality improvements, at the cost of an increased computation effort either in the offline or the online phase. Overall, our methods provide multiple options to balance the solution quality/time trade-off, suiting a variety of practical application scenarios. To test our methods, we ground our approaches on two real cases studies with both offline and online decisions: an energy management problem with uncertain renewable generation and demand, and a vehicle routing problem with uncertain travel times. The application domains feature respectively continuous and discrete decisions. An extensive analysis of the experimental results shows that indeed offline/online integration may lead to substantial benefits.
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- 2021
10. HADA: An automated tool for hardware dimensioning of AI applications.
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De Filippo, Allegra, Borghesi, Andrea, Boscarino, Andrea, and Milano, Michela
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ARTIFICIAL intelligence , *ONLINE algorithms , *DEEP learning , *MACHINE learning , *HARDWARE - Abstract
In recent years, the uptake of Artificial Intelligence (AI) in industry is increasing. For many AI techniques, like Deep Learning, optimization, planning, etc., computational and storage requirements are significant. The problem of determining what is the right hardware (HW on premise or on the cloud) architecture and its dimensioning for AI algorithms is still crucial. Searching for the optimal solution is often challenging, as it is not trivial to anticipate the behavior of an algorithm on diverse architectures. This is especially true if the AI application must respect quality-of-service constraints or budgets. In this scenario, having an automated decision support tool to match algorithms, user constraints and HW resources would be a great advantage for companies and practitioners working with AI applications. In this paper, we tackle this challenge with an approach that relies on the Empirical Model Learning paradigm, based on the integration of Machine Learning (ML) models into an optimization problem. The key idea is to integrate domain knowledge held by experts with data-driven models that learn the relationships between HW requirements and AI algorithm performances. In particular, the approach starts with benchmarking multiple AI algorithms on different HW resources, generating data used to train ML models; then, optimization is used to find the best HW configuration that respects user-defined constraints (e.g., budget, time, solution quality). In the experimental evaluation we validate our approach on a complex problem, namely online algorithms for energy systems, an area characterized by uncertainty and tight HW and real-time constraints. Results show the effectiveness of our approach and its flexibility: We can train the ML models only once and reuse them in the optimization model to tackle a variety of problems, determined by different data instances and user-defined constraints. [ABSTRACT FROM AUTHOR]
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- 2022
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11. An Analysis of Regularized Approaches for Constrained Machine Learning
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Michela Milano, Andrea Borghesi, Federico Baldo, Michele Lombardi, Heintz F., Milano M., O'Sullivan B., Lombardi, Michele, Baldo, Federico, Borghesi, Andrea, and Milano, Michela
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer science ,Computer Science - Artificial Intelligence ,Machine Learning (stat.ML) ,02 engineering and technology ,Machine learning ,computer.software_genre ,Regularization (mathematics) ,GeneralLiterature_MISCELLANEOUS ,Machine Learning (cs.LG) ,Machine Learning ,Statistics - Machine Learning ,020204 information systems ,Regularization ,0202 electrical engineering, electronic engineering, information engineering ,business.industry ,Trustworthiness ,Artificial Intelligence (cs.AI) ,Constraints ,Domain knowledge ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Machine Learning, Constrained Machine Learning, Regularization Methods ,computer - Abstract
Regularization-based approaches for injecting constraints in Machine Learning (ML) were introducedto improve a predictive model via expert knowledge. Given the recent interest in ethical and trustworthy AI, however, several works are resorting to these approaches for the (conceptually different) task of enforcing desired properties over a ML model, e.g. fairness. When regularized methods are used to enforce constraints, a typical approach consists in adjusting the regularizer multipliersuntil a suitable compromise between accuracy and constraint satisfaction is reached(e.g. a discrimination index becomes sufficiently low). This approach enables the use of traditional training algorithms, at the cost of having to search over the space of possible multipliers.Though the method is known to work well in many practical cases, the process has been subject to little general analysis. With this note, we make a preliminary step in this direction, providing a more systematic overview of the strengths and (in particular) potential weaknesses of this class of approaches.  
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- 2020
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12. Exploiting Incomparability in Solution Dominance: Improving General Purpose Constraint-Based Mining
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Koçak, Gökberk, Akgün, Özgür, Guns, Tias, Miguel, Ian, DeGiacomo, G, Catala, A, Dilkina, B, Milano, M, Barro, S, Bugarin, A, Lang, J, De Giacomo, Giuseppe, Catala, Alejandro, Dilkina, Bistra, Milano, Michela, Barro, Senén, Bugarín, Alberto, Lang, Jérôme, University of St Andrews. School of Computer Science, University of St Andrews. Centre for Interdisciplinary Research in Computational Algebra, Barro, Senen, Bugarin, Alberto, Lang, Jerome, Data Analytics Laboratory, and Business technology and Operations
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QA75 ,QA75 Electronic computers. Computer science ,~DC~ ,NDAS ,artificial intelligence ,BDC ,R2C - Abstract
In data mining, finding interesting patterns is a challenging task. Constraint-based mining is a well-known approach to this, and one for which constraint programming has been shown to be a well-suited and generic framework. Constraint dominance programming (CDP) has been proposed as an extension that can capture an even wider class of constraint-based mining problems, by allowing us to compare relations between patterns. In this paper we improve CDP with the ability to specify an incomparability condition. This allows us to overcome two major shortcomings of CDP: finding dominated solutions that must then be filtered out after search, and unnecessarily adding dominance blocking constraints between incomparable solutions. We demonstrate the efficacy of our approach by extending the problem specification language ESSENCE and implementing it in a solver-independent manner on top of the constraint modelling tool CONJURE. Our experiments on pattern mining tasks with both a CP solver and a SAT solver show that using the incomparability condition during search significantly improves the efficiency of dominance programming and reduces (and often eliminates entirely) the need for post-processing to filter dominated solutions. Publisher PDF
- Published
- 2020
13. Empirical decision model learning
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Michela Milano, Andrea Bartolini, Michele Lombardi, Lombardi, Michele, Milano, Michela, and Bartolini, Andrea
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Artificial neural network ,Linguistics and Language ,Decision support system ,Combinatorial optimization ,Complex system ,Computer science ,Modulo ,0211 other engineering and technologies ,Decision tree ,02 engineering and technology ,Machine learning ,computer.software_genre ,Language and Linguistics ,Mixed integer non-linear programming ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Constraint programming ,Prescriptive analytics ,Language and Linguistic ,SAT modulo theorie ,021103 operations research ,business.industry ,Local search ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer ,Decision model - Abstract
One of the biggest challenges in the design of real-world decision support systems is coming up with a good combinatorial optimization model. Often enough, accurate predictive models (e.g. simulators) can be devised, but they are too complex or too slow to be employed in combinatorial optimization. In this paper, we propose a methodology called Empirical Model Learning (EML) that relies on Machine Learning for obtaining components of a prescriptive model, using data either extracted from a predictive model or harvested from a real system. In a way, EML can be considered as a technique to merge predictive and prescriptive analytics. All models introduce some form of approximation. Citing G.E.P. Box [1] “Essentially, all models are wrong, but some of them are useful”. In EML, models are useful if they provide adequate accuracy, and if they can be effectively exploited by solvers for finding high-quality solutions. We show how to ground EML on a case study of thermal-aware workload dispatching. We use two learning methods, namely Artificial Neural Networks and Decision Trees and we show how to encapsulate the learned model in a number of optimization techniques, namely Local Search, Constraint Programming, Mixed Integer Non-Linear Programming and SAT Modulo Theories. We demonstrate the effectiveness of the EML approach by comparing our results with those obtained using expert-designed models.
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- 2017
14. Boosting Combinatorial Problem Modeling with Machine Learning
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Michele Lombardi, Michela Milano, Lombardi, Michele, and Milano, Michela
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FOS: Computer and information sciences ,021103 operations research ,Boosting (machine learning) ,Optimization problem ,Exploit ,Computer Science - Artificial Intelligence ,business.industry ,Computer science ,0211 other engineering and technologies ,02 engineering and technology ,Constraint satisfaction ,Machine learning ,computer.software_genre ,Artificial Intelligence (cs.AI) ,Pervasive technology ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Combinatorial optimization ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer - Abstract
In the past few years, the area of Machine Learning (ML) has witnessed tremendous advancements, becoming a pervasive technology in a wide range of applications. One area that can significantly benefit from the use of ML is Combinatorial Optimization. The three pillars of constraint satisfaction and optimization problem solving, i.e., modeling, search, and optimization, can exploit ML techniques to boost their accuracy, efficiency and effectiveness. In this survey we focus on the modeling component, whose effectiveness is crucial for solving the problem. The modeling activity has been traditionally shaped by optimization and domain experts, interacting to provide realistic results. Machine Learning techniques can tremendously ease the process, and exploit the available data to either create models or refine expert-designed ones. In this survey we cover approaches that have been recently proposed to enhance the modeling process by learning either single constraints, objective functions, or the whole model. We highlight common themes to multiple approaches and draw connections with related fields of research., Comment: Originally submitted to IJCAI2018
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- 2018
15. Methods for off-line/on-line optimization under uncertainty
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Michele Lombardi, Allegra De Filippo, Michela Milano, De Filippo, Allegra, Lombardi, Michele, and Milano, Michela
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021103 operations research ,Computer science ,Artificial Intelligence ,0211 other engineering and technologies ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,02 engineering and technology ,Line (text file) ,Algorithm ,Off line - Abstract
In this work we present two general techniques to deal with multi-stage optimization problems under uncertainty, featuring off-line and on-line decisions. The methods are applicable when: 1) the uncertainty is exogenous; 2) there exists a heuristic for the on-line phase that can be modeled as a parametric convex optimization problem. The first technique replaces the on-line heuristics with an anticipatory solver, obtained through a systematic procedure. The second technique consists in making the off-line solver aware of the on-line heuristic, and capable of controlling its parameters so as to steer its behavior. We instantiate our approaches on two case studies: an energy management system with uncertain renewable generation and load demand, and a vehicle routing problem with uncertain travel times. We show how both techniques achieve high solution quality w.r.t. an oracle operating under perfect information, by obtaining different trade-offs in terms of computation time.
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- 2018
16. From Offline to Online Kidney Exchange Optimization
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Michela Milano, Michele Lombardi, Danuta Sorina Chisca, Barry O'Sullivan, Chisca, Danuta, Lombardi, Michele, Milano, Michela, and O'Sullivan, Barry
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Optimization ,Kidney Exchange, Optimization, Operations Research, Anticipatory Algorithms ,Computer science ,media_common.quotation_subject ,0211 other engineering and technologies ,02 engineering and technology ,Scientific literature ,Machine learning ,computer.software_genre ,Kidney ,Patient diagnosis ,Stochastic online optimization problem ,Mathematical model ,Stochastic processes ,Anticipatory algorithm ,0202 electrical engineering, electronic engineering, information engineering ,Organ failure ,Quality (business) ,Optimisation ,Offline kidney exchange problem ,Donor pool generation ,Online stochastic kidney exchange ,On-line anticipatory algorithm ,media_common ,Transplantation ,021103 operations research ,Basis (linear algebra) ,Stochastic process ,business.industry ,Scalability ,Conferences ,Approximation algorithm ,Kidney exchange programs ,Online kidney exchange optimization ,Approximation algorithms ,Donor-patient pairs ,020201 artificial intelligence & image processing ,Surgery ,Artificial intelligence ,business ,computer ,Decision making - Abstract
Kidney exchange programs enable willing, but incompatible, donor-patient pairs to swap donors, thus allowing persons suffering from organ failure to access transplantation. Choosing which pairs to match requires solving a stochastic online optimization problem where patients and donors arrive over time. Despite this, most of the related scientific literature has focused on deterministic offline models. In this paper, we present a simple approach to employ a model for the offline Kidney Exchange Problem (KEP) as the basis of an on-line anticipatory algorithm. Our approach grounds on existing techniques for the on-line KEP, but it generalizes them and provides a more accurate estimate of the expected impact of current decisions. In an experimentation based on a state-of-the-art donor pool generation method, the approach provides improvements in terms of quality and is able to deal with realistic instance size in reasonable time.
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- 2018
17. Model Agnostic Solution of CSPs via Deep Learning: A Preliminary Study
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Paola Mello, Andrea Galassi, Michele Lombardi, Michela Milano, DIPARTIMENTO DI INFORMATICA - SCIENZA E INGEGNERIA, Facolta' di INGEGNERIA, Da definire, AREA MIN. 09 - Ingegneria industriale e dell'informazione, Willem-Jan van Hoeve, and Galassi Andrea, Lombardi Michele, Mello Paola, Milano Michela
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Structure (mathematical logic) ,search ,021103 operations research ,constraints solving ,business.industry ,Computer science ,Process (engineering) ,Deep learning ,0211 other engineering and technologies ,model learning, deep neural networks, constraints solving, search ,02 engineering and technology ,Construct (python library) ,Variable (computer science) ,model learning ,deep neural networks ,Line (geometry) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,business - Abstract
none 4 no Deep Neural Networks (DNNs) have been shaking the AI scene, for their ability to excel at Machine Learning tasks without relying on complex, hand-crafted, features. Here, we probe whether a DNN can learn how to construct solutions of a CSP, without any explicit symbolic information about the problem constraints. We train a DNN to extend a feasible solution by making a single, globally consistent, variable assignment. The training is done over intermediate steps of the construction of feasible solutions. From a scientific standpoint, we are interested in whether a DNN can learn the structure of a combinatorial problem, even when trained on (arbitrarily chosen) construction sequences of feasible solutions. In practice, the network could also be used to guide a search process, e.g. to take into account (soft) constraints that are implicit in past solutions or hard to capture in a traditional declarative model. This research line is still at an early stage, and a number of complex issues remain open. Nevertheless, we already have intriguing results on the classical Partial Latin Square and N-Queen completion problems. mixed Galassi Andrea, Lombardi Michele, Mello Paola, Milano Michela Galassi Andrea, Lombardi Michele, Mello Paola, Milano Michela
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- 2018
18. Sustainable Policy Making: A Strategic Challenge for Artificial Intelligence
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Barry O'Sullivan, Michela Milano, Marco Gavanelli, Milano, Michela, O'Sullivan, Barry, and Gavanelli, Marco
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Sustainable development ,Engineering ,Decision support system ,Knowledge management ,business.industry ,Process (engineering) ,Sentiment analysis ,Ambientale ,Context (language use) ,Constraint Programming ,Sustainable Policy Making ,Politics ,Game Theory ,Artificial Intelligence ,Strategic Environmental Assessment ,media_common.cataloged_instance ,Artificial intelligence ,European union ,business ,Game theory ,media_common - Abstract
Policy making is an extremely complex process occurring in changing environments and affecting the three pillars of sustainable development: society, economy and the environment. Each political decision in fact implies some form of social reactions, it affects economic and financial aspects and has substantial environmental impacts. Improving decision making in this context could have a huge beneficial impact on all these aspects. There are a number of Artificial Intelligence techniques that could play an important role in improving the policy making process such as decision support and optimization techniques, game theory, data and opinion mining and agent-based simulation. We outline here some potential use of AI technology as it emerged by the European Union (EU) EU FP7 project ePolicy: Engineering the Policy Making Life-Cycle, and we identify some potential research challenges.
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- 2014
19. Solving Mathematical Puzzles: A Challenging Competition for AI
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Federico Chesani, Michela Milano, Paola Mello, Chesani, Federico, Mello, Paola, and Milano, Michela
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Computer science ,Process (engineering) ,business.industry ,05 social sciences ,Artificial Intelligence, mathematical puzzles, challenge ,Context (language use) ,06 humanities and the arts ,Mathematical puzzle ,0603 philosophy, ethics and religion ,Competition (economics) ,Artificial Intelligence ,060302 philosophy ,0502 economics and business ,Artificial intelligence ,business ,050203 business & management - Abstract
Recently, a number of noteworthy results have been achieved in various fields of artificial intelligence, and many aspects of the problem solving process have received significant attention by the scientific community. In this context, the extraction of comprehensive knowledge suitable for problem solving and reasoning, from textual and pictorial problem descriptions, has been less investigated, but recognized as essential for autonomous thinking in Artificial Intelligence. In this work we present a challenge where methods and tools for deep understanding are strongly needed for enabling problem solving: we propose to solve mathematical puzzles by means of computers, starting from text and diagrams describing them, without any human intervention. We are aware that the proposed challenge is hard and of difficult solution nowadays (and in the foreseeable future), but even studying and solving only single parts of the proposed challenge would represent an important step forward for artificial intelligence.
- Published
- 2017
20. Report from the Editor in Chief, year 2015
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Michela Milano and Milano, Michela
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Engineering ,Computational Theory and Mathematics ,business.industry ,Artificial Intelligence ,Computational Theory and Mathematic ,Editor in chief ,Discrete Mathematics and Combinatorics ,business ,GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries) ,GeneralLiterature_MISCELLANEOUS ,Software ,Discrete Mathematics and Combinatoric ,Management - Abstract
I started my term as Editor in Chief of the Constraint Journal on the 1st of January 2015. The purpose of this short report is to provide some news about the journal to the Editorial and Advisory board members and to the constraint programming community.
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- 2017
21. DARDIS: Distributed and randomized DIspatching and scheduling
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Michele Lombardi, Andrea Bartolini, Luca Benini, Michela Milano, Thomas Bridi, Bridi, Thoma, Lombardi, Michele, Bartolini, Andrea, Benini, Luca, and Milano, Michela
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020203 distributed computing ,Distributed scheduling ,Job shop scheduling ,Computer science ,Distributed computing ,Computation ,Computer Science (all) ,02 engineering and technology ,Load balancing (computing) ,computer.software_genre ,Supercomputer ,Theoretical Computer Science ,Scheduling (computing) ,Grid computing ,Artificial Intelligence ,Scalability ,0202 electrical engineering, electronic engineering, information engineering ,Soft/Hard constraint ,Variable constraint ,020201 artificial intelligence & image processing ,Start time ,computer - Abstract
Scheduling and dispatching are critical enabling technologies in supercomputing and grid computing. In these contexts, scalability is an issue: we have to allocate and schedule upi?źto tens of thousands of tasks on tens of thousands of resources. This problem scale is out of reach for complete and centralized scheduling approaches. We propose a distributed allocation and scheduling paradigm called DARDIS that is lightweight, scalable and fully customizable in many domains. In DARDIS each task offloads to the available resources the computation of a probability index associated with each possible start time for the given task on the specific resource. The task then selects the proper resource and start time on the basis of the above probability. The scheduler can be customized with different policies to fit several objective functions like load balancing or makespan. We evaluate our approach in the domain of grids and supercomputers. We compare DARDIS with the most widely used algorithms used in these specific domains to show that this approach can reach better solutions in several cases.
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- 2016
22. Emerging architectures for global system science
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Michela Milano, Pascal Van Hentenryck, Miiano, Michela, Milano, Michela, and Van Hentenryck, Pascal
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Artificial Intelligence ,General Medicine ,Software - Abstract
Our society is organized around a number of (interdependent) global systems. Logistic and supply chains, health services, energy networks, financial markets, computer networks, and cities are just a few examples of such global, complex systems. These global systems are socio-technical and involve interactions between complex infrastructures, man-made processes, natural phenomena, multiple stakeholders, and human behavior. For the first time in the history of manking, we have access to data sets of unprecedented scale and accuracy about these infrastructures, processes, natural phenomena, and human behaviors. In addition, progress in high-performancing computing, data mining, machine learning, and decision support opens the possibility of looking at these problems more holistically, capturing many of these aspects simultaneously. This paper addresses emergent architectures enabling controlling, predicting and reaoning on these systems.
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- 2015
23. Swarm-based Traffic Lights Policy Selection
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Riccardo Belletti, Luca Foschini, Michela Milano, Alessio Bonfietti, Daniel Krajzewicz, Belletti, Riccardo, Bonfietti, Alessio, Foschini, Luca, Milano, Michela, and Krajzewicz, Daniel
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Limited Penetration Rate ,Engineering ,V2V Communications ,business.industry ,Frame (networking) ,SIGNAL (programming language) ,Real-time computing ,Swarm behaviour ,Transportation ,Traffic flow ,Swarm intelligence ,Transport engineering ,Computer Networks and Communication ,Control theory ,Artificial Intelligence ,Control and Systems Engineering ,Vehicular Peer-to-Peer Cooperation ,Control system ,Automotive Engineering ,business ,Intersection (aeronautics) ,SUMO & ns-3 Simulations ,Traffic Monitoring - Abstract
This paper presents a Traffic Lights control system, inspired by Swarm intelligence methodologies, in which every intersection controller makes independent decisions to pursue common goals and is able to improve global traffic performance. The solution is low cost and widely applicable to different urban scenarios. This work is developed within the COLOMBO European project. Control methods are divided into macroscopic and microscopic control level methods. The former react to macroscopic key figures such as mean congestion length and mean traffic density and acts on the signal program choice or the development of the frame signal program. The latter include changes at short notice based on changes in the traffic flow: they include methods for signal program adaptation and development. The developed system has been widely tested on synthetic benchmarks with promising results.
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- 2014
24. Strategic decision making on complex systems
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Michele Lombardi, Michela Milano, Milano, Michela, and Lombardi, Michele
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Mechanism design ,Combinatorial optimization ,Exploit ,Complex system ,Computer science ,Management science ,Process (engineering) ,Large scale problem ,Context (language use) ,Industrial engineering ,Computational Theory and Mathematics ,Artificial Intelligence ,Computational Theory and Mathematic ,Machine learning ,Constraint programming ,Discrete Mathematics and Combinatorics ,Game theory ,Hybrid optimization ,Simulation ,Software - Abstract
In this paper, we propose a challenging research direction for Constraint Programming and optimization techniques in general. We address problems where decisions to be taken affect and are affected by complex systems, which exhibit phenomena emerging from a collection of interacting objects, capable to self organize and to adapt their behaviour according to their history and feedback. Such systems are unfortunately impervious to modeling efforts via state-of-the-art combinatorial optimization techniques. We provide some hints on approaches to connect and integrate decision making and optimization technology with complex systems via machine learning, game theory and mechanism design. In the first case, the aim is to extract modeling components to express the relation between global decisions and observables emerging from the real system, or from an accurate predictive model (e.g. a simulator). In the second case, the idea is to exploit game theory, mechanism design and distributed decision making to drive the process toward realistic equilibrium points avoiding globally optimal, but unrealistic, configurations. We conclude by observing how dealing with the complexity of the considered problems will require to greatly extend the capabilities of state of the art solvers: in this context, we identify some key issues and highlight future research directions.
- Published
- 2014
25. Looking into the crystal-ball: A bright future for CP
- Author
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Michela Milano, Pascal Van Hentenryck, Milano, Michela, and Van Hentenryck, Pascal
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business.industry ,Computer science ,Viewpoints ,Structuring ,Data science ,Software ,Computational Theory and Mathematics ,Artificial Intelligence ,Schema (psychology) ,Research community ,Computational Theory and Mathematic ,Forward looking ,Constraint programming ,Snapshot (computer storage) ,Discrete Mathematics and Combinatorics ,business - Abstract
An interesting exercise for a research community is to foresee its future, identify key research challenges and open problems and be forward-looking in both technology and application fields. The Constraint Programming community, in more than 20 years, has performed this exercise several times. One example is the recent book on Trends in Constraint Programming edited in 2007 by Benhamou, Jussien and O’Sullivan [1] and the recent challenge paper at AAAI 2012 by O’Sullivan [17] entitled Opportunities and challenges for Constraint Programming. Other examples are the earlier papers by Van Hentenryck and Saraswat [8] on Strategic Directions in Constraint Programming in 1996 and the ACM 50th Anniversary Issue of ACM Computing Surveys of the same year on Strategic Directions in Computing Research containing a number of forward looking papers [2–7, 9–16, 18–21]. Some of these papers have correctly foreseen the future, some challenges have received less then expected attention, but overall these papers have always provided a useful guide for researchers and newcomers. Here we are offering another snapshot of the constraint programming community and what its members consider as promising directions. We have intentionally invited contribution without structuring them into a schema. Rather we have preferred a bottom-up call for proposals providing supported-by-evidence viewpoints on future directions of CP.
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- 2014
26. An Overview of AI Research in Italy
- Author
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Andrea Roli, Michela Milano, M. BRAMER, Roli, Andrea, and Milano, Michela
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Structure (mathematical logic) ,Computer science ,Research areas ,ARTIFICIAL INTELLIGENCE ,Automated reasoning ,Data science ,GeneralLiterature_MISCELLANEOUS - Abstract
This contribution is aimed at providing an overview of the main Italian research areas and activities. In this chapter we first analyze the collaboration structure of Italian research, that involves more than eight hundred scholars and researchers from both the University and the Industry and, from a network perspective, it appears to be scale-free. Then, we briefly illustrate the main subjects of investigation and applications. AI research in Italy traces back to the '70s with an increase in the last twenty years and spans the main research AI areas, from automated reasoning and ontologies, to machine learning, robotics and evolutionary computation.
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- 2009
27. Filtering Algorithms for the NValue Constraint
- Author
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Zeynep Kiziltan, Toby Walsh, Christian Bessiere, Brahim Hnich, Emmanuel Hebrard, Agents, Apprentissage, Contraintes (COCONUT), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), National ICT Australia [Sydney] (NICTA), Cork Constraint Computation Centre (4C UCC), University College Cork (UCC), Department of Electronics, Computer Science and Systems [Bologne] (DEIS), Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO), Izmir University of Economics (IUE), M. Cadoli, M. Gavanelli, T. Mancini, C. Bessiere, E. Hebrard, B. Hnich, Z. Kiziltan, T. Walsh, Barták, Roman, and Milano Michela
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Intersection Graph ,Maximal Match ,0102 computer and information sciences ,02 engineering and technology ,NP-hard ,01 natural sciences ,Upper and lower bounds ,AtMostNValue ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Discrete Mathematics and Combinatorics ,[INFO]Computer Science [cs] ,Constraint satisfaction problem ,Mathematics ,Numerical analysis ,Constrained optimization ,Relaxation (iterative method) ,NValue constraint ,Constraint satisfaction ,Pruning ,Constraint (information theory) ,AtleastNValue ,Linear Relaxation ,Computational Theory and Mathematics ,010201 computation theory & mathematics ,Independent set ,Local consistency ,Combinatorial optimization ,020201 artificial intelligence & image processing ,Global constraints ,Constraint Satisfaction Problem ,Heuristics ,Algorithm ,Software ,Interval Graph - Abstract
International audience; The constraint NValue counts the number of different values assigned to a vector of variables. Propagating generalized arc consistency on this constraint is NP-hard. We show that computing even the lower bound on the number of values is NP-hard. We therefore study different approximation heuristics for this problem. We introduce three new methods for computing a lower bound on the number of values. The first two are based on the maximum independent set problem and are incomparable to a previous approach based on intervals. The last method is a linear relaxation of the problem. This gives a tighter lower bound than all other methods, but at a greater asymptotic cost.
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- 2005
28. Anomaly detection using autoencoders in high performance computing systems
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Michele Lombardi, Luca Benini, Andrea Borghesi, Michela Milano, Andrea Bartolini, Borghesi, Andrea, Bartolini, Andrea, Lombardi, Michele, Milano, Michela, and Benini, Luca
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Artificial neural network ,business.industry ,Computer science ,Computer Science - Artificial Intelligence ,Anomaly Detection, HPC Systems, Autoencoders, Machine Learning, Supercomputer, Applications of AI ,02 engineering and technology ,General Medicine ,Machine learning ,computer.software_genre ,Supercomputer ,Autoencoder ,Machine Learning (cs.LG) ,Set (abstract data type) ,Artificial Intelligence (cs.AI) ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Key (cryptography) ,020201 artificial intelligence & image processing ,Anomaly detection ,Artificial intelligence ,business ,computer - Abstract
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states). We propose a novel approach for anomaly detection in High Performance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder. The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with). We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%)., 9 pages, 3 figures
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