Object-centric proto-symbolic behavioural reasoning from pixels (2411.17438v2)

Abstract: Autonomous intelligent agents must bridge computational challenges at disparate levels of abstraction, from the low-level spaces of sensory input and motor commands to the high-level domain of abstract reasoning and planning. A key question in designing such agents is how best to instantiate the representational space that will interface between these two levels -- ideally without requiring supervision in the form of expensive data annotations. These objectives can be efficiently achieved by representing the world in terms of objects (grounded in perception and action). In this work, we present a novel, brain-inspired, deep-learning architecture that learns from pixels to interpret, control, and reason about its environment, using object-centric representations. We show the utility of our approach through tasks in synthetic environments that require a combination of (high-level) logical reasoning and (low-level) continuous control. Results show that the agent can learn emergent conditional behavioural reasoning, such as $(A \to B) \land (\neg A \to C)$, as well as logical composition $(A \to B) \land (A \to C) \vdash A \to (B \land C)$ and XOR operations, and successfully controls its environment to satisfy objectives deduced from these logical rules. The agent can adapt online to unexpected changes in its environment and is robust to mild violations of its world model, thanks to dynamic internal desired goal generation. While the present results are limited to synthetic settings (2D and 3D activated versions of dSprites), which fall short of real-world levels of complexity, the proposed architecture shows how to manipulate grounded object representations, as a key inductive bias for unsupervised learning, to enable behavioral reasoning.

• The paper presents the OBR that autonomously learns and applies complex conditional rules from raw pixel data.
• It uses iterative variational inference to derive object-centric representations, enabling unsupervised cognitive reasoning.
• It outperforms deep reinforcement learning baselines, demonstrating robust adaptability in dynamic synthetic environments.

Object-Centric Proto-Symbolic Behavioral Reasoning from Pixels: An Overview

The paper presents a novel, brain-inspired neural architecture called the Object-centric Behavioral Reasoner (OBR), designed to enable object-based cognitive reasoning and control from pixel data. This architecture advances the development of autonomous agents that are capable of reasoning about and interacting with their environment through unsupervised learning using object-centric representations.

Summary and Core Contributions

OBR operates by forming a bridge between low-level sensory data and high-level cognitive tasks, learning from raw pixel data without the need for labeled inputs. The system is characterized by its ability to perform unsupervised learning, allowing the internal structure of the agent's cognitive model to emerge naturally from object interactions within its operational environment. The model can execute logical reasoning processes, effectively generating behaviors that conform to learned conditional rules.

Key to the architecture is its iterative variational inference strategy, drawing inspiration from iterative amortized inference techniques. This enables OBR to derive object-centric latent representations and generate dynamic behavior without supervised labels. The architecture is evaluated primarily in synthetic environments constructed to assess its capacity for both reasoning and continuous control. Notably, OBR demonstrates significant autonomy in learning and applying conditional logic rules such as $(A \to B) \land (\neg A \to C)$, logical composition, and XOR operations.

Technical Achievements and Evaluation

The paper details several core achievements facilitated by OBR's architecture:

1. Autonomous Rule Learning: The architecture showcases its ability to learn complex conditional rules from interactions with its environment. For instance, the system can apply rules based on the presence of specific visual cues (like objects with certain shapes) and execute predefined behaviors accordingly.
2. Robustness and Adaptability: OBR is capable of adapting to changes within its environment thanks to its ongoing inference processes, which accommodate unexpected variations and generalize to a varying number of objects.
3. Unsupervised Preference Learning: The preference network, a significant component of the architecture, learns the agent's desired internal state from latent representations directly. This network functions without direct supervision, determining goals in an embedded space that defines desired future states.
4. Efficient Performance: Extensive evaluation against baseline methodologies such as deep reinforcement learning (DRL) approaches demonstrates OBR's efficacy. DRL methods, even with dense reward structures and access to true object states, fail to match OBR's proficiency at rule-based reasoning purely from pixel inputs.

Theoretical and Practical Implications

The OBR architecture exemplifies the integration of high-level cognitive decision-making with environment-driven behavior modeling, establishing a novel class of neural architectures that support proto-symbolic reasoning. This approach addresses the architectural gap between perceptual input and cognitive outputs, enabling systems to construct abstract reasoning models that guide their interaction with the physical world.

Practically, OBR could significantly impact fields such as robotics, where autonomous systems must perform complex reasoning in partially observable environments. The robustness of the architecture to varied object representations and its adaptability accentuates its potential for real-world applications, where supervision and data labeling are impractical or costly.

Speculative Directions for Future Developments

OBR sets a foundation for further exploration into hybrid models that combine unsupervised learning paradigms with symbolic AI. Future research could enhance the interactions between latent spaces and high-dimensional action spaces, improving the reasoning processes for more complex and realistic tasks. Moreover, incorporating real-world sensory modalities, such as tactile feedback in robotics, could advance the architecture's applicability, enhancing its ability to operate within more diverse environments.

Overall, this paper makes a substantial contribution to the field of AI by proposing a scalable and unsupervised architecture for conditional reasoning from pixel data, modeling behavior in a way that closely aligns with principles observed in human cognition. The insights gained from OBR's development may spur new lines of research that seek to further elucidate the intersection of cognitive science and artificial intelligence design.