Spain

Many important AI problems involve an optimization component. Maximum satisfiability (MaxSAT) is an optimization version of Boolean satisfiability (SAT) that can be applied to a variety of computationally hard optimization problems that arise in AI. Constituting a declarative approach to optimization, modern state-of-the-art MaxSAT solvers allow for efficiently tackling NP-hard optimization problems arising from different applications via simple propositional encodings. This can be much less time-consuming than building a customized algorithmic solution specific to a particular optimization problem. Indeed, a well-engineered, generic MaxSAT solver can often be more efficient than a custom solution. Furthermore, MaxSAT offers an alternative to the more classical declarative optimization paradigm of integer programming (IP), especially in the many AI applications where the underlying problems are naturally represented with logical constraints. In this tutorial, we will introduce MaxSAT, give an overview of the different techniques that have been developed for solving MaxSAT, focusing on recent advances and covering both complete and incomplete MaxSAT solving, and explain via case studies how MaxSAT has been used to solve problems from different areas of AI. We will also outline major current research directions in MaxSAT. After this tutorial, you should be better equipped to apply modern MaxSAT technology in real-world settings.

ECAI 2020

Advances in Maximum Satisfiability

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Answer Set Programming: From Theory to Practice

Advances in Maximum Satisfiability

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Advances in Maximum Satisfiability

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Next from ECAI 2020

Answer Set Programming: From Theory to Practice

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Local Justice and Machine Learning: Modeling and Inferring Dynamic Ethical Preferences toward Allocations

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