| 1. |
- Bessiere, Christian, et al.
(författare)
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The Inductive Constraint Programming Loop
- 2017
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Ingår i: IEEE Intelligent Systems. - New York : Institute of Electrical and Electronics Engineers (IEEE). - 1541-1672 .- 1941-1294. ; 32:5, s. 44-52
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Tidskriftsartikel (refereegranskat)abstract
- Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, that we call the Inductive Constraint Programming loop. In this approach data is gathered and analyzed systematically, in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand.
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| 2. |
- Bessiere, Christian, et al.
(författare)
-
The Inductive Constraint Programming Loop
- 2016
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Ingår i: Data Mining and Constraint Programming. - Cham : Springer International Publishing. - 9783319501369 - 9783319501376 ; , s. 303-309
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Bokkapitel (refereegranskat)abstract
- Constraint programming is used for a variety of real-world optimisa-tion problems, such as planning, scheduling and resource allocation prob-lems. At the same time, one continuously gathers vast amounts of dataabout these problems. Current constraint programming software does notexploit such data to update schedules, resources and plans. We propose anew framework, that we call theInductive Constraint Programming loop.In this approach data is gathered and analyzed systematically, in order todynamically revise and adapt constraints and optimization criteria. In-ductive Constraint Programming aims at bridging the gap between theareas of data mining and machine learning on the one hand, and constraintprogramming on the other hand.
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| 3. |
- Nanni, Mirco, et al.
(författare)
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Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
- 2020
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Ingår i: Transactions on Data Privacy. - : Institut d'Investigació en Intel·ligència Artificial. - 1888-5063 .- 2013-1631. ; 23, s. 1-6
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Tidskriftsartikel (refereegranskat)abstract
- The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates - if and when they want and for specific aims - with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.
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| 4. |
- Pedreschi, Dino, et al.
(författare)
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Towards a Social Artificial Intelligence
- 2023
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Ingår i: Human-Centered Artificial Intelligence. - : Springer. - 9783031243486 - 9783031243493 ; , s. 415-428
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Konferensbidrag (refereegranskat)abstract
- Artificial Intelligence can both empower individuals to face complex societal challenges and exacerbate problems and vulnerabilities, such as bias, inequalities, and polarization. For scientists, an open challenge is how to shape and regulate human-centered Artificial Intelligence ecosystems that help mitigate harms and foster beneficial outcomes oriented at the social good. In this tutorial, we discuss such an issue from two sides. First, we explore the network effects of Artificial Intelligence and their impact on society by investigating its role in social media, mobility, and economic scenarios. We further provide different strategies that can be used to model, characterize and mitigate the network effects of particular Artificial Intelligence driven individual behavior. Secondly, we promote the use of behavioral models as an addition to the data-based approach to get a further grip on emerging phenomena in society that depend on physical events for which no data are readily available. An example of this is tracking extremist behavior in order to prevent violent events. In the end, we illustrate some case studies in-depth and provide the appropriate tools to get familiar with these concepts.
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