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James Fox

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8 papers
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8

UAI Conference 2024 Conference Paper

Characterising Interventions in Causal Games

  • Manuj Mishra
  • James Fox
  • Michael J. Wooldridge

Causal games are probabilistic graphical models that enable causal queries to be answered in multi-agent settings. They extend causal Bayesian networks by specifying decision and utility variables to represent the agents’ degrees of freedom and objectives. In multi-agent settings, whether each agent decides on their policy before or after knowing the causal intervention is important as this affects whether they can respond to the intervention by adapting their policy. Consequently, previous work in causal games imposed chronological constraints on permissible interventions. We relax this by outlining a sound and complete set of primitive causal interventions so the effect of any arbitrarily complex interventional query can be studied in multi-agent settings. We also demonstrate applications to the design of safe AI systems by considering causal mechanism design and commitment.

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NeurIPS Conference 2024 Conference Paper

Measuring Goal-Directedness

  • Matt MacDermott
  • James Fox
  • Francesco Belardinelli
  • Tom Everitt

We define maximum entropy goal-directedness (MEG), a formal measure of goal-directedness in causal models and Markov decision processes, and give algorithmsfor computing it. Measuring goal-directedness is important, as it is a criticalelement of many concerns about harm from AI. It is also of philosophical interest, as goal-directedness is a key aspect of agency. MEG is based on an adaptation ofthe maximum causal entropy framework used in inverse reinforcement learning. Itcan measure goal-directedness with respect to a known utility function, a hypothesisclass of utility functions, or a set of random variables. We prove that MEG satisfiesseveral desiderata and demonstrate our algorithms with small-scale experiments.

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AAAI Conference 2024 Conference Paper

Reasoning about Causality in Games (Abstract Reprint)

  • Lewis Hammond
  • James Fox
  • Tom Everitt
  • Ryan Carey
  • Alessandro Abate
  • Michael Wooldridge

Causal reasoning and game-theoretic reasoning are fundamental topics in artificial intelligence, among many other disciplines: this paper is concerned with their intersection. Despite their importance, a formal framework that supports both these forms of reasoning has, until now, been lacking. We offer a solution in the form of (structural) causal games, which can be seen as extending Pearl's causal hierarchy to the game-theoretic domain, or as extending Koller and Milch's multi-agent influence diagrams to the causal domain. We then consider three key questions: i) How can the (causal) dependencies in games – either between variables, or between strategies – be modelled in a uniform, principled manner? ii) How may causal queries be computed in causal games, and what assumptions does this require? iii) How do causal games compare to existing formalisms? To address question i), we introduce mechanised games, which encode dependencies between agents' decision rules and the distributions governing the game. In response to question ii), we present definitions of predictions, interventions, and counterfactuals, and discuss the assumptions required for each. Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support. Finally, we highlight possible applications of causal games, aided by an extensive open-source Python library.

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ECAI Conference 2023 Conference Paper

Learning Task Automata for Reinforcement Learning Using Hidden Markov Models

  • Alessandro Abate
  • Yousif Almulla
  • James Fox
  • David Hyland
  • Michael J. Wooldridge

Training reinforcement learning (RL) agents using scalar reward signals is often infeasible when an environment has sparse and non-Markovian rewards. Moreover, handcrafting these reward functions before training is prone to misspecification. We learn non-Markovian finite task specifications as finite-state ‘task automata’ from episodes of agent experience within environments with unknown dynamics. First, we learn a product MDP, a model composed of the specification’s automaton and the environment’s MDP (both initially unknown), by treating it as a partially observable MDP and employing a hidden Markov model learning algorithm. Second, we efficiently distil the task automaton (assumed to be a deterministic finite automaton) from the learnt product MDP. Our automaton enables a task to be decomposed into sub-tasks, so an RL agent can later synthesise an optimal policy more efficiently. It is also an interpretable encoding of high-level task features, so a human can verify that the agent’s learnt tasks have no misspecifications. Finally, we also take steps towards ensuring that the automaton is environment-agnostic, making it well-suited for use in transfer learning.

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LAMAS&SR Workshop 2023 Workshop Paper

Learning Task Automata for Reinforcement Learning using Hidden Markov Models

  • James Fox

Training reinforcement learning (RL) agents using scalar reward signals is often infeasible when an environment has sparse and non-Markovian rewards. Moreover, handcrafting these reward functions before training is prone to misspecification. This work proposes a novel pipeline for learning non-Markovian finite task specifications as finite-state task automata from episodes of agent experience within unknown environments. First, we learn a product MDP, a model composed of the specification’s automaton and the environment’s MDP (both initially unknown), by treating it as a partially observable MDP and employing algorithms for hidden Markov models. Second, we propose a novel method for distilling the task automaton (assumed to be a deterministic finite automaton - DFA) from the learnt product MDP. Our learnt task automaton enables a task to be decomposed into sub-tasks, so an RL agent can later synthesise an optimal policy more efficiently. It is also an interpretable encoding of high-level task features, so a human can verify that the agent has learnt tasks with no misspecifications. We also take steps towards ensuring that the learnt automaton is environment-agnostic, making it well-suited for use in transfer learning.

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TARK Conference 2023 Conference Paper

On Imperfect Recall in Multi-Agent Influence Diagrams

  • James Fox
  • Matt MacDermott
  • Lewis Hammond
  • Paul Harrenstein
  • Alessandro Abate
  • Michael J. Wooldridge

Multi-agent influence diagrams (MAIDs) are a popular game-theoretic model based on Bayesian networks. In some settings, MAIDs offer significant advantages over extensive-form game representations. Previous work on MAIDs has assumed that agents employ behavioural policies, which set independent conditional probability distributions over actions for each of their decisions. In settings with imperfect recall, however, a Nash equilibrium in behavioural policies may not exist. We overcome this by showing how to solve MAIDs with forgetful and absent-minded agents using mixed policies and two types of correlated equilibrium. We also analyse the computational complexity of key decision problems in MAIDs, and explore tractable cases. Finally, we describe applications of MAIDs to Markov games and team situations, where imperfect recall is often unavoidable.

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AIJ Journal 2023 Journal Article

Reasoning about causality in games

  • Lewis Hammond
  • James Fox
  • Tom Everitt
  • Ryan Carey
  • Alessandro Abate
  • Michael Wooldridge

Causal reasoning and game-theoretic reasoning are fundamental topics in artificial intelligence, among many other disciplines: this paper is concerned with their intersection. Despite their importance, a formal framework that supports both these forms of reasoning has, until now, been lacking. We offer a solution in the form of (structural) causal games, which can be seen as extending Pearl's causal hierarchy to the game-theoretic domain, or as extending Koller and Milch's multi-agent influence diagrams to the causal domain. We then consider three key questions: i) How can the (causal) dependencies in games – either between variables, or between strategies – be modelled in a uniform, principled manner? ii) How may causal queries be computed in causal games, and what assumptions does this require? iii) How do causal games compare to existing formalisms? To address question i), we introduce mechanised games, which encode dependencies between agents' decision rules and the distributions governing the game. In response to question ii), we present definitions of predictions, interventions, and counterfactuals, and discuss the assumptions required for each. Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support. Finally, we highlight possible applications of causal games, aided by an extensive open-source Python library.

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AAMAS Conference 2021 Conference Paper

Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and Practice

  • Lewis Hammond
  • James Fox
  • Tom Everitt
  • Alessandro Abate
  • Michael Wooldridge

Multi-agent influence diagrams (MAIDs) are a popular form of graphical model that, for certain classes of games, have been shown to offer key complexity and explainability advantages over traditional extensive form game (EFG) representations. In this paper, we extend previous work on MAIDs by introducing the concept of a MAID subgame, as well as subgame perfect and trembling hand perfect equilibrium refinements. We then prove several equivalence results between MAIDs and EFGs. Finally, we describe an open source implementation for reasoning about MAIDs and computing their equilibria.

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