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Elija Perrier

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TMLR Journal 2025 Journal Article

Infrastructure for AI Agents

  • Alan Chan
  • Kevin Wei
  • Sihao Huang
  • Nitarshan Rajkumar
  • Elija Perrier
  • Seth Lazar
  • Gillian K Hadfield
  • Markus Anderljung

\textbf{AI agents} plan and execute interactions in open-ended environments. For example, OpenAI's Operator can use a web browser to do product comparisons and buy online goods. To facilitate beneficial interactions and mitigate harmful ones, much research focuses on directly modifying agent behaviour. For example, developers can train agents to follow user instructions. This focus on direct modifications is useful, but insufficient. We will also need external protocols and systems that shape how agents interact with institutions and other actors. For instance, agents will need more efficient protocols to communicate with each other and form agreements. In addition, attributing an agent's actions to a particular human or other legal entity can help to establish trust, and also disincentivize misuse. Given this motivation, we propose the concept of \textbf{agent infrastructure}: technical systems and shared protocols external to agents that are designed to mediate and influence their interactions with and impacts on their environments. Just as the Internet relies on protocols like HTTPS, our work argues that agent infrastructure will be similarly indispensable to ecosystems of agents. We identify three functions for agent infrastructure: 1) attributing actions, properties, and other information to specific agents, their users, or other actors; 2) shaping agents' interactions; and 3) detecting and remedying harmful actions from agents. We provide an incomplete catalog of research directions for such functions. For each direction, we include analysis of use cases, infrastructure adoption, relationships to existing (internet) infrastructure, limitations, and open questions. Making progress on agent infrastructure can prepare society for the adoption of more advanced agents.

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AAAI Conference 2019 Conference Paper

Deep Hierarchical Graph Convolution for Election Prediction from Geospatial Census Data

  • Mike Li
  • Elija Perrier
  • Chang Xu

Geographic information systems’ (GIS) research is widely used within the social and physical sciences and plays a crucial role in the development and implementation by governments of economic, education, environment and transportation policy. While machine learning methods have been applied to GIS datasets, the uptake of powerful deep learning CNN methodologies has been limited in part due to challenges posed by the complex and often poorly structured nature of the data. In this paper, we demonstrate the utility of GCNNs for GIS analysis via a multi-graph hierarchical spatial-filter GCNN network model in the context of GIS systems to predict election outcomes using socio-economic features drawn from the 2016 Australian Census. We report a marked improvement in performance accuracy of Hierarchical GCNNs over benchmark generalised linear models and standard GCNNs, especially in semi-supervised tasks. These results indicate the widespread potential for GIS-GCNN research methods to enrich socio-economic GIS analysis, aiding the social sciences and policy development.

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