Summary of Synthesizing Evolving Symbolic Representations For Autonomous Systems, by Gabriele Sartor et al.
Synthesizing Evolving Symbolic Representations for Autonomous Systems
by Gabriele Sartor, Angelo Oddi, Riccardo Rasconi, Vieri Giuliano Santucci, Rosa Meo
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Symbolic Computation (cs.SC)
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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 Recently, AI systems have made significant progress in various tasks through Deep Reinforcement Learning (DRL). Researchers introduced Intrinsic Motivation (IM) to the RL mechanism, which simulates the agent’s curiosity and encourages it to explore interesting areas. This feature has proved vital for learning policies without specific goals. To better understand the knowledge collected by agents, classical planning formalism was used in recent research to represent the knowledge acquired and reach extrinsic goals. PPDDL demonstrated usefulness in reviewing gathered knowledge, making causal correlations explicit, and finding plans to reach states faced during experience. This work presents a new architecture implementing an open-ended learning system that synthesizes its experience into a PPDDL representation, updating it over time. The system integrates IM to explore the environment self-directedly, exploiting high-level knowledge acquired during experience. It iteratively discovers options, explores using them, abstracts collected knowledge, and plans. This paper proposes an alternative approach to open-ended learning architectures, combining low- and high-level representations in a virtuous loop. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recently, AI systems have made big progress. Researchers found a way for machines to learn new things without being told exactly what to do. They used something called Intrinsic Motivation, which makes the machine curious and want to explore more. This helped the machine learn better. To understand what the machine learned, scientists used a special tool that helps make sense of all the information. The tool is called PPDDL, and it’s very useful for reviewing what the machine learned, making connections between things, and finding ways to achieve goals. In this paper, researchers created a new way for machines to learn and improve over time. They combined low- and high-level thinking to make the machine more curious and better at learning. |
Keywords
» Artificial intelligence » Reinforcement learning
