Learning Symbolic Models of Stochastic Domains (1110.2211v1)

Abstract: In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics.

• The paper introduces a supervised learning algorithm for AI agents to learn probabilistic, relational planning models (NDRs) of complex, noisy, and nondeterministic environments.
• The framework utilizes deictic references and a probabilistic approach to model action effects and incorporate noise, enabling generalization across objects and circumstances.
• Empirical validation shows these learned models outperform propositional and standard relational models, improving planning task scores in challenging simulated domains.

Overview of "Learning Symbolic Models of Stochastic Domains"

The paper, "Learning Symbolic Models of Stochastic Domains," authored by Hanna M. Pasula, Luke S. Zettlemoyer, and Leslie Pack Kaelbling, discusses the development of probabilistic, relational planning rule representations for modeling complex, noisy, and nondeterministic action effects in artificial agents. These models allow agents to learn and adapt dynamically to a stochastic environment without requiring a predefined manual model of the world. The authors employ a supervised learning algorithm to derive these models through observation of action outcomes, thereby balancing between model complexity and likelihood of accurately capturing the observed data.

Core Contributions

The key contribution of this paper is the introduction of a learning algorithm capable of generating accurate symbolic models, termed Noisy Deictic Rules (NDRs). These models integrate probabilistic reasoning with logical abstractions to form a compact and efficient representation for capturing dynamic action effects.

1. Probabilistic and Relational Framework: The framework uses deictic references, noise handling, and language extensions to address the inherent complexity and uncertainty in the domains being modeled.
2. Action and Domain Modeling: Actions are represented with relational formulations using variable abstraction, enabling them to generalize over different objects and circumstances within the world. The models accommodate actions affecting not just specifically named objects but relationally referred ones.
3. Noise Incorporation: The paper acknowledges that in complex environments not all outcomes can be explicitly captured and introduces a method for accounting for unpredictable events, termed as 'noise outcomes'.
4. Empirical Validation: The model's effectiveness is validated through experiments in simple constructed environments and a sophisticated 3D blocks world simulator. The evaluation shows that the models not only capture the intended dynamics but also effectively plan to achieve specified goals.

Numerical and Experimental Insights

In the experimental domain involving a slippery gripper and logistics with trucks and drivers, the authors demonstrate that the deictic representations outperform propositional and standard relational models, particularly under conditions with limited training data. The usage of NDR dramatically reduced noise modeling precision, resulting in significant improvements in planning task scores.

Implications and Future Work

The development of probabilistic symbolic models has a substantial impact on enhancing the autonomy and adaptability of AI systems. Particularly, this formulation addresses the scalability issues of planning and learning algorithms when exposed to high-fidelity simulated environments reflective of real-world physics and interactions.

The authors propose that future advancements could involve enhancing the scalability and dynamic capabilities of these models to operate in even more complex, partially observable environments. They also envision developing online learning algorithms that integrate exploration strategies to more effectively refine these models in real-time.

Concluding Remarks

The work represented by this paper is a stride toward more dynamically adept AI systems, providing a robust framework for agents to learn and adapt to environments of varying complexity without exhaustive pre-specification. While the challenges of stochastic domains are manifold, the strategies presented are pivotal in enabling autonomous systems to act, learn, and optimize in such environments.