Open-World Learning for Radically Autonomous Agents
Pat Langley
Abstract
In this paper, I pose a new research challenge – to develop intelligent agents that exhibit radical autonomy by responding to sudden, long-term changes in their environments. I illustrate this idea with examples, identify abilities that support it, and argue that, although each ability has been studied in isolation, they have not been combined into integrated systems. In addition, I propose a framework for characterizing environments in which goal-directed physical agents operate, along with specifying the ways in which those environments can change over time. In closing, I outline some approaches to the empirical study of such open-world learning.
Topics & Concepts
Computer scienceAutonomyClosing (real estate)Isolation (microbiology)Autonomous agentOpen researchHuman–computer interactionData scienceArtificial intelligenceCognitive scienceKnowledge managementPsychologyBusinessPolitical scienceWorld Wide WebMicrobiologyLawFinanceBiologyReinforcement Learning in RoboticsAI-based Problem Solving and PlanningData Stream Mining Techniques