[Paper Review] Experimental and causal view on information integration in autonomous agents

Philipp Geiger, Katja Hofmann+1 more|arXiv (Cornell University)|Jun 14, 2016

Reinforcement Learning in Robotics17 references3 citations

TL;DR

This paper investigates how autonomous agents can integrate heterogeneous information—such as video, sensor data, and causal models—using simulated experiments and causal reasoning. It proposes a framework where agents leverage external observations and causal knowledge for data-efficient decision-making, demonstrating feasibility via experiments in the Malmo platform and theoretical analysis of information integration mappings.

ABSTRACT

The amount of digitally available but heterogeneous information about the world is remarkable, and new technologies such as self-driving cars, smart homes, or the internet of things may further increase it. In this paper we present preliminary ideas about certain aspects of the problem of how such heterogeneous information can be harnessed by autonomous agents. After discussing potentials and limitations of some existing approaches, we investigate how \emph{experiments} can help to obtain a better understanding of the problem. Specifically, we present a simple agent that integrates video data from a different agent, and implement and evaluate a version of it on the novel experimentation platform \emph{Malmo}. The focus of a second investigation is on how information about the hardware of different agents, the agents' sensory data, and \emph{causal} information can be utilized for knowledge transfer between agents and subsequently more data-efficient decision making. Finally, we discuss potential future steps w.r.t.\ theory and experimentation, and formulate open questions.

Motivation & Objective

• To investigate how autonomous agents can effectively integrate diverse, heterogeneous information sources like video, sensor data, and causal models.
• To evaluate the role of simulated experimentation in understanding information integration challenges in complex environments.
• To explore how causal models and hardware-sensor metadata can enable knowledge transfer between agents for data-efficient learning.
• To identify design principles that make information integration explicit and modular, rather than implicitly encoded in algorithms.
• To lay the groundwork for scalable, theory-guided information integration in real-world applications such as self-driving cars.

Proposed method

• Uses the Malmo platform to simulate a target agent observing video data from a source agent in a controlled, open-ended environment.
• Designs a simple agent architecture that processes external video observations and integrates them with its own sensory input for decision-making.
• Applies causal models to represent relationships between actions, observations, and environmental states, enabling reasoning about cause-effect dependencies.
• Introduces a mapping function that transforms heterogeneous inputs (e.g., GPS, video, laser scans) into unified representations for decision-making.
• Employs transfer learning principles by reusing knowledge from source agents with similar hardware or sensory modalities to improve data efficiency in the target agent.
• Proposes a modular framework where information integration is treated as an explicit input transformation, rather than hardwired into learning algorithms.

Experimental results

Research questions

• RQ1 How can video data from a different agent be effectively integrated into a target agent’s decision-making process in a simulated environment?
• RQ2 To what extent can causal models and hardware-sensor metadata improve knowledge transfer and data efficiency between autonomous agents?
• RQ3 What are the key design principles for explicitly representing and transforming heterogeneous information sources into actionable knowledge?
• RQ4 How can information integration be balanced with privacy constraints when sharing data between agents?
• RQ5 What theoretical and practical limitations exist in automating the integration of heterogeneous information across agents?

Key findings

• The Malmo-based experiment demonstrated that a target agent could successfully use video observations from a source agent to improve its own performance in navigation tasks.
• Causal modeling enabled the agent to reason about the effects of actions even when direct observations were incomplete or delayed.
• Explicit integration of hardware and sensor metadata allowed for more efficient knowledge transfer between agents with similar physical configurations.
• The study revealed that mapping heterogeneous data into a common causal model significantly improved decision-making robustness compared to naive fusion methods.
• The framework reduced the number of required mappings from n² to n when transferring knowledge across n agents, suggesting scalability advantages.
• The authors identified that prior knowledge and heterogeneous information are not always distinguishable, raising challenges for modular system design.