As artificial intelligence (AI) continues to advance, researchers at the University of Chinese Academy of Sciences are exploring the concept of foundation agents, which are AI systems capable of open-ended decision-making tasks in both physical and virtual worlds. These foundation agents could be the next paradigm shift in decision-making, similar to how large language models (LLMs) have revolutionized language and knowledge-based tasks.
One of the key advantages of foundation agents is their ability to adapt quickly to new tasks with minimal fine-tuning or prompting. This is in stark contrast to traditional AI decision-making approaches that rely on manual rules or extensive training for each new task. Foundation agents could provide a more versatile and efficient solution for decision-making in various fields, addressing the limitations of brittle and task-specific AI systems.
So, what are the characteristics that make foundation agents unique? According to the researchers, there are three fundamental characteristics:
1. **Unified Representation:** Foundation agents have a unified representation of environment states, agent actions, and feedback signals. This allows them to perceive and understand the world in a multi-modal way, bridging the gap between different variables involved in decision-making.
2. **Unified Policy Interface:** These agents have a unified policy interface that can be applied to various tasks and domains, from robotics to healthcare. This flexibility enables them to adapt to different decision-making scenarios, making them more versatile than traditional AI systems.
3. **Reasoning Abilities:** Foundation agents make decisions based on reasoning about world knowledge, the environment, and interactions with other agents. This reasoning process allows them to navigate complex environmental information and choose optimal actions.
To develop foundation agents, the researchers propose a roadmap consisting of three key components:
1. **Data Collection:** Large-scale interactive data needs to be collected from both the internet and physical environments. In cases where real-world data is scarce or risky to obtain, simulators and generative models can be used.
2. **Pre-training:** Foundation agents are pre-trained on unlabeled data to learn decision-related knowledge representations. This pre-training phase enables the agents to adapt to new tasks with minimal examples during the customization phase.
3. **Alignment with Language Models:** Foundation agents must be aligned with large language models to integrate world knowledge and human values. This integration enhances their perception, adaptation, and reasoning abilities.
Developing foundation agents comes with its own set of challenges. Unlike language and vision models, which focus on high-level abstractions, foundation agents need to consider low-level details of the physical world. There is also a significant domain gap between different decision-making scenarios, making it challenging to develop a unified policy interface. However, recent advances in robotics and self-driving cars have shown promising results in bridging these gaps.
In the field of robotics, researchers are combining control systems with foundation models to create versatile systems that can handle previously unseen situations. Additionally, self-driving cars are leveraging large language models to integrate commonsense knowledge and human cognitive abilities into autonomous driving systems. Other domains such as healthcare and science could also benefit from the capabilities of foundation agents.
Overall, foundation agents have the potential to revolutionize decision-making in the real world. Their enhanced perception, adaptation, and reasoning abilities address the limitations of conventional AI systems and unlock new possibilities for tackling complex decision-making tasks. As AI continues to advance, the development of foundation agents could lead to significant breakthroughs in various fields.
