Transforming task representations to perform novel tasks (2005.04318v3)

Abstract: An important aspect of intelligence is the ability to adapt to a novel task without any direct experience (zero-shot), based on its relationship to previous tasks. Humans can exhibit this cognitive flexibility. By contrast, models that achieve superhuman performance in specific tasks often fail to adapt to even slight task alterations. To address this, we propose a general computational framework for adapting to novel tasks based on their relationship to prior tasks. We begin by learning vector representations of tasks. To adapt to new tasks, we propose meta-mappings, higher-order tasks that transform basic task representations. We demonstrate the effectiveness of this framework across a wide variety of tasks and computational paradigms, ranging from regression to image classification and reinforcement learning. We compare to both human adaptability and language-based approaches to zero-shot learning. Across these domains, meta-mapping is successful, often achieving 80-90% performance, without any data, on a novel task, even when the new task directly contradicts prior experience. We further show that meta-mapping can not only generalize to new tasks via learned relationships, but can also generalize using novel relationships unseen during training. Finally, using meta-mapping as a starting point can dramatically accelerate later learning on a new task, and reduce learning time and cumulative error substantially. Our results provide insight into a possible computational basis of intelligent adaptability and offer a possible framework for modeling cognitive flexibility and building more flexible artificial intelligence systems.

• The paper proposes a meta-mapping framework that transforms task representations for novel, zero-shot task adaptation without direct training.
• It leverages learned relationships between tasks through shared network components, accelerating learning and reducing errors.
• Experimental results across domains, including polynomials, card games, and reinforcement learning, demonstrate robust generalization.

Transforming Task Representations to Perform Novel Tasks

Introduction

The paper "Transforming task representations to perform novel tasks" addresses the challenge of zero-shot task adaptation by proposing a framework based on meta-mappings. The key idea is to exploit the relationship between novel and prior tasks by transforming task representations, allowing models to perform new tasks without direct training experience.

Meta-Mapping Framework

Meta-mapping involves the transformation of basic task representations into representations for new tasks. This is achieved by leveraging learned relationships between tasks. The process starts with encoding tasks as vector representations. Meta-mappings, which are higher-order transformations, then modify these representations based on relationships derived from prior knowledge.

Figure 1: Meta-mapping can adapt to a new polynomial zero-shot, based on its relationship to prior polynomials.

Architectural Details

The proposed architecture is designed to handle both basic tasks and meta-mapping transformations. It consists of several key components:

• Perception Network ($\mathcal{P}$): Processes task inputs into a shared representational space $Z$.
• Hyper Network ($\mathcal{H}$): Adapts task-specific network parameters based on task representations.
• Task Network ($\mathcal{T}$): Executes task-specific computations based on adapted parameters.
• Example and Language Networks ($\mathcal{E}, \mathcal{L}$): Construct task and meta-mapping representations from examples or language inputs, respectively.

The architecture facilitates both the execution of basic tasks from examples and the transformation of these tasks via meta-mappings using shared network components, promoting efficient generalization.

Experiments and Results

The framework was evaluated across several domains, including polynomial transformations, card games, visual concept recognition, and reinforcement learning (RL). Key findings include:

1. Polynomials: Meta-mapping effectively performed zero-shot adaptation, achieving high accuracy on held-out transformed polynomials.

Figure 2: Visualizing how meta-mappings systematically transform the model's polynomial representations.

1. Card Games: Comparing to human adaptability, the model generalized well when switching from winning to losing strategies using meta-mappings.
2. Visual Concepts: Meta-mappings allowed zero-shot generalization of visual classification tasks. The model generalized well to held-out meta-mappings after experiencing sufficient training examples.

   Figure 3: Comparing meta-mapping to human adaptation in simple card games.

3. Reinforcement Learning: Meta-mapping improved zero-shot task performance in RL tasks, successfully adapting to novel tasks such as role reversals (e.g., switching rewards/punishments).

   Figure 4: Comparing RL adaptation performance when meta-mapping with different task representations.

Implementation Insights

• Homoiconicity: The architecture's capability to use the same networks for both task and meta-mapping representations allows the model to leverage structural similarities, enhancing generalization.
• Task Representation Optimization: Meta-mapping provides a beneficial starting point for further learning, significantly accelerating learning and reducing errors compared to random initializations.

  Figure 5: Meta-mapping provides a good starting point for later learning.

Conclusion

The meta-mapping framework provides a novel approach to zero-shot task adaptation by transforming task representations based on prior task relationships. It achieves robust generalization across various domains, demonstrating the potential for building more adaptable AI systems. Future work could explore more complex task representations and extensions to richer environments.