IBM announces 2019 Call For Code grand prize winner

#artificialintelligence

IBM today announced the 2019 Call for Code grand prize was awarded to Prometeo for developing a health monitoring platform for firefighters. The Barcelona-based team consisting of a nurse, a firefighter, and three developers will receive $200,000 and assistance from IBM and its partners to bring the project to life. TNW's finance, blockchain, and business event is coming up soon Promoteo began as an endeavor by firefighter Joan Herrera. Realizing there were no systems in place to monitor the health of firefighters combating wildfires, Herrera and nurse Vicenç Padró began collecting data by hand. Eventually, they joined forces with three IT professionals, Salomé Valero, Josep Ràfols, and Marco Rodriguez, and the team joined the Call For Code challenge.


IBM Names 5 Finalists in 2019 Call for Code Challenge

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AsTeR (Europe) – During natural disasters, emergency call centers are overwhelmed and lack the human resources to deal with the sudden uptick in calls. Project AsTeR helps prioritize these calls based on their level of emergency. Instead of being directly connected to an operator, victims are asked to briefly explain their emergency over the phone. Their responses are then converted to text and analyzed to extract key information, such as the number of victims, type of emergency and location. AsTeR then provides first responders with a map identifying areas with high levels of emergency based on the number of people involved and the type of injuries.



Agent-Based Negotiation Teams

AAAI Conferences

Agent-based negotiation teams are negotiation parties formed by more than a single individual. Individuals unite as a single negotiation party because they share a common goal that is related to a negotiation with one or several opponents. My research goal is providing agent-based computational models for negotiation teams in multi-agent systems.


Temporal Defeasible Argumentation in Multi-Agent Planning

AAAI Conferences

In this paper, I present my ongoing research on temporal defeasible argumentation-based multi-agent planning. In multi-agent planning a team of agents share a set of goals but have diverse abilities and temporal beliefs, which vary over time. In order to plan for these goals, agents start a stepwise dialogue consisting of exchanges of temporal plan proposals, plus temporal arguments against them, where both, actions with different duration, and temporal defeasible arguments, need to be integrated. This thesis proposes a computational framework for this research on multi-agent planning.