pure-mas result in briefhome.ufam.edu.br/hiramaral/04_siape_final_2016/siape_bibliotec… ·...

PURE-MAS Result In Brief Project reference: 275217 Funded under: FP7-PEOPLE Country: Netherlands Related information Report Summary Final Report Summary - PURE-MAS (Planning under uncertainty for real- world multiagent systems) Building a team of intelligent agents Multi-agent systems that perceive their environment and act are at the heart of artificial intelligence. An EU- funded project studied automated planning under uncertainty — how agents can manage decision making when their environment is partially observable. From autonomous vehicles to vehicular ad hoc networks or smart grids, intelligent distributed systems can be found everywhere. For an isolated agent, planning under uncertainty has already been thoroughly studied. However, centralised methods for single agents clearly do not suffice for large-scale multi-agent systems. Funded by the EU, the 'Planning under uncertainty for real-world multi-agent systems' (PURE-MAS) project addressed the issue of the multi-agent decision process. Research was based on a variety of multi-agent models for cooperative agents. These included decentralised partially observable Markov decision process (dec-POMDP) and other related models known as multi-agent sequential decision making. Scientists improved scalability both in terms of planning horizon and number of agents. By proposing one of the fastest optimal dec-POMDP planners, scientists were able to compute previously unattainable planning horizons. Furthermore, through exploiting their structure, important advances were made for scaling up planning to unprecedented team sizes. Besides scalability, PURE-MAS explored two types of event-based representations for capturing relevant changes in the environment, and as such, providing a higher-level abstraction. This paves the way for online planning agents that can adapt to the dynamic environment by continuously modifying plans during plan execution. Several algorithms were also proposed for optimising communication between agents. By combining game theory and planning, the project team achieved coordinated planning amongst single agents. In particular, techniques drawn from dynamic mechanism design helped align the incentives of multiple contractors. Based on these techniques, human players can learn that cooperation amongst competitors can be beneficial and road authorities can draw up better maintenance contracts. Another project achievement was to demonstrate a small team of robots that could successfully track a mobile target. PURE- MAS also optimised the multi-agent system to provide intelligence to a distributed smart grid. PURE-MAS contributed significantly to achieving one of the major goals of artificial intelligence, namely how to build many intelligent agents that perceive their environment and execute appropriate actions. All project advances can find applications in transportation systems, smart grids and surveillance cameras. © Thinkstock Page 1 of 2 Research and Innovation

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Page 1: PURE-MAS Result In Briefhome.ufam.edu.br/hiramaral/04_SIAPE_FINAL_2016/SIAPE_Bibliotec… · PURE-MAS Result In Brief Project reference: 275217 Funded under: FP7-PEOPLE Country: Netherlands

PURE-MAS Result In BriefProject reference: 275217Funded under: FP7-PEOPLECountry: Netherlands

Related information

Report Summary Final Report Summary - PURE-MAS (Planning under uncertainty for real-world multiagent systems)

Building a team of intelligent agents Multi-agent systems that perceive their environment and act are at the heart of artificial intelligence. An EU-funded project studied automated planning under uncertainty — how agents can manage decision making when their environment is partially observable.

From autonomous vehicles to vehicular ad hoc networks or smart grids, intelligent distributed systems can be found everywhere. For an isolated agent, planning under uncertainty has already been thoroughly studied. However, centralised methods for single agents clearly do not suffice for large-scale multi-agent systems.

Funded by the EU, the 'Planning under uncertainty for real-world multi-agent systems' (PURE-MAS) project addressed the issue of the multi-agent decision process. Research was based on a variety of multi-agent models for cooperative agents. These included decentralised partially observable Markov decision process (dec-POMDP) and other related models known as multi-agent

sequential decision making.

Scientists improved scalability both in terms of planning horizon and number of agents. By proposing one of the fastest optimal dec-POMDP planners, scientists were able to compute previously unattainable planning horizons. Furthermore, through exploiting their structure, important advances were made for scaling up planning to unprecedented team sizes.

Besides scalability, PURE-MAS explored two types of event-based representations for capturing relevant changes in the environment, and as such, providing a higher-level abstraction. This paves the way for online planning agents that can adapt to the dynamic environment by continuously modifying plans during plan execution. Several algorithms were also proposed for optimising communication between agents.

By combining game theory and planning, the project team achieved coordinated planning amongst single agents. In particular, techniques drawn from dynamic mechanism design helped align the incentives of multiple contractors. Based on these techniques, human players can learn that cooperation amongst competitors can be beneficial and road authorities can draw up better maintenance contracts.

Another project achievement was to demonstrate a small team of robots that could successfully track a mobile target. PURE-MAS also optimised the multi-agent system to provide intelligence to a distributed smart grid.

PURE-MAS contributed significantly to achieving one of the major goals of artificial intelligence, namely how to build many intelligent agents that perceive their environment and execute appropriate actions. All project advances can find applications in transportation systems, smart grids and surveillance cameras.

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Researchand Innovation

http://cordis.europa.eu/project/rcn/100395_en.html

http://cordis.europa.eu/programme/rcn/848_en.html

http://cordis.europa.eu/result/rcn/149964_en.html