SOURCE-LINKED INTELLIGENCE

Specializing TEmporal Planning using Reinforcement Learning

CORDIS · observation · Publication date unknown

of the basic forms of intelligence. Temporal planning studies the automated synthesis of strategies when time and temporal constraints matter. Temporal planning is one of the most strategic fields of Artificial Intelligence, with applications in autonomous robotics, logistics, flexible production, and many other fields. Historically, the research on temporal planning follows a general-purpose framework: a generic engine searches for the strategy by reasoning on the problem statement (i.e. the starting condition and the desired objective), as well as on a formal model of the domain (i.e. the possible actions). Despite substantial progress in the recent years, domain-independent temporal planning still suffers from scalability issues, and fails to deal with real-word problems. The alternative is to devise ad-hoc, domain-specific solutions that, although efficient, are costly to develop, rigid to maintain, and often inapplicable in non-nominal situations. STEP-RL will study the foundations of a new approach to Temporal Planning, that is domain-independent and efficient at the same tim

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recordType

award

status

SIGNED

region

EU

value

1493750

unit

EUR