Open-World Learning for Radically Autonomous Agents
| Autor: | Pat Langley |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science media_common.quotation_subject Autonomous agent 02 engineering and technology General Medicine computer.software_genre Convolutional neural network Intelligent agent Empirical research Human–computer interaction 020204 information systems 0202 electrical engineering electronic engineering information engineering Isolation (psychology) Information system 020201 artificial intelligence & image processing computer Autonomy media_common |
| Zdroj: | AAAI |
| ISSN: | 2374-3468 2159-5399 |
| DOI: | 10.1609/aaai.v34i09.7078 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|