Spain

ECAI 2020

Tackling Imitative Attacker Deception in Repeated Bayesian Stackelberg Security Games

stackelberg games

imitative deception

bayesian security games

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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A ticket is required to attend this event, please register using the link below:

https://aamas2022-conference.auckland.ac.nz/attending/registration/

Registration Is Required

AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

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.

STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment

Item Response Theory to Estimate the Latent Ability of Speech Synthesizers

New computational models for the Choquet Integral

What makes a Better Companion? Towards Social & Engaging Peer Learning

Transformer-based Argument Mining for Healthcare Applications

Lifted Heuristics for Timeline-based Planning

Macro Operator Synthesis for ADL Domains

Verification of Recurrent Neural Networks for Cognitive Tasks via Reachability Analysis

Reuse, Reduce and Recycle: Optimizing Reiter’s HS-Tree for Sequential Diagnosis

On Partial Multi-Task Learning

https://d3smihljt9218e.cloudfront.net/transcript/1138/subtitle/370cfb1bf9fd46c9e4abdaffb25e1be7.vtt

English (automatic)

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STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment

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Robust No-Regret Learning in Min-Max Stackelberg Games

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Next from ECAI 2020

STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment

Similar lecture

Robust No-Regret Learning in Min-Max Stackelberg Games

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