Summary of Simulating Human-like Daily Activities with Desire-driven Autonomy, by Yiding Wang et al.
Simulating Human-like Daily Activities with Desire-driven Autonomy
by Yiding Wang, Yuxuan Chen, Fangwei Zhong, Long Ma, Yizhou Wang
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning paper introduces a Desire-driven Autonomous Agent (D2A) capable of autonomously proposing and selecting tasks, driven by satisfying its multi-dimensional desires. The agent’s motivational framework is based on a dynamic Value System inspired by the Theory of Needs, incorporating human-like desires such as social interaction, personal fulfillment, and self-care. The agent evaluates its current state, proposes candidate activities, and selects the one that best aligns with its intrinsic motivations. Experimental results demonstrate the agent generates coherent, contextually relevant daily activities while exhibiting variability and adaptability similar to human behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a robot that can decide what to do without being told exactly what to do. This is called autonomy, which is important for humans. Right now, AI agents need clear instructions or rewards to behave correctly. But what if we could make them more like us? A new paper introduces an “agent” that chooses its own tasks based on what it wants, like social interaction or personal fulfillment. The agent uses a special system to decide what’s most important at the moment and makes choices accordingly. This helps the agent be more creative and flexible, just like humans. |
Keywords
» Artificial intelligence » Machine learning
