Table of Contents
Fetching ...

Policy-Adaptable Methods For Resolving Normative Conflicts Through Argumentation and Graph Colouring

Johnny Joyce

TL;DR

This work tackles normative conflicts in multi-agent systems by proposing a policy-adaptable framework that merges argumentation with graph colouring to extract admissible, non-conflicting norm sets. It introduces ColourResolve and ColourResolveComplete, along with curtailment-based ColourCurtail and ColourCurtailComplete, and proves admissibility and completeness under argumentation semantics. The authors define flexible heuristics to realize lexicographic policies (e.g., lex posterior, lex superior, lex specialis) and demonstrate how curtailment enables admitting multiple, potentially conflicting norms in a controlled manner, with an overall worst-case complexity of $O(n^3)$. Empirical comparisons against Oren et al.'s work show competitive performance and highlight the practical value of the approach for safe reinforcement learning and real-world autonomous systems, where policy-adaptable, coherent normative reasoning is critical.

Abstract

In a multi-agent system, one may choose to govern the behaviour of an agent by imposing norms, which act as guidelines for how agents should act either all of the time or in given situations. However, imposing multiple norms on one or more agents may result in situations where these norms conflict over how the agent should behave. In any system with normative conflicts (such as safe reinforcement models or systems which monitor safety protocols), one must decide which norms should be followed such that the most important and most relevant norms are maintained. We introduce a new method for resolving normative conflicts through argumentation and graph colouring which is compatible with a variety of normative conflict resolution policies. We prove that this method always creates an admissible set of arguments under argumentation semantics, meaning that it produces coherent outputs. We also introduce more robust variants of this method, each building upon their predecessor to create a superior output, and we include further mathematical proof of their coherence. Our most advanced variant uses the existing concept of curtailment, where one norm may supersede another without fully eliminating it. The methods we introduce are all compatible with various pre-existing policies for resolving normative conflicts. Empirical evaluations are also performed to compare our algorithms to each other and to others in existing literature.

Policy-Adaptable Methods For Resolving Normative Conflicts Through Argumentation and Graph Colouring

TL;DR

This work tackles normative conflicts in multi-agent systems by proposing a policy-adaptable framework that merges argumentation with graph colouring to extract admissible, non-conflicting norm sets. It introduces ColourResolve and ColourResolveComplete, along with curtailment-based ColourCurtail and ColourCurtailComplete, and proves admissibility and completeness under argumentation semantics. The authors define flexible heuristics to realize lexicographic policies (e.g., lex posterior, lex superior, lex specialis) and demonstrate how curtailment enables admitting multiple, potentially conflicting norms in a controlled manner, with an overall worst-case complexity of . Empirical comparisons against Oren et al.'s work show competitive performance and highlight the practical value of the approach for safe reinforcement learning and real-world autonomous systems, where policy-adaptable, coherent normative reasoning is critical.

Abstract

In a multi-agent system, one may choose to govern the behaviour of an agent by imposing norms, which act as guidelines for how agents should act either all of the time or in given situations. However, imposing multiple norms on one or more agents may result in situations where these norms conflict over how the agent should behave. In any system with normative conflicts (such as safe reinforcement models or systems which monitor safety protocols), one must decide which norms should be followed such that the most important and most relevant norms are maintained. We introduce a new method for resolving normative conflicts through argumentation and graph colouring which is compatible with a variety of normative conflict resolution policies. We prove that this method always creates an admissible set of arguments under argumentation semantics, meaning that it produces coherent outputs. We also introduce more robust variants of this method, each building upon their predecessor to create a superior output, and we include further mathematical proof of their coherence. Our most advanced variant uses the existing concept of curtailment, where one norm may supersede another without fully eliminating it. The methods we introduce are all compatible with various pre-existing policies for resolving normative conflicts. Empirical evaluations are also performed to compare our algorithms to each other and to others in existing literature.
Paper Structure (23 sections, 2 theorems, 4 equations, 11 figures, 5 algorithms)

This paper contains 23 sections, 2 theorems, 4 equations, 11 figures, 5 algorithms.

Key Result

Proposition 1

Let $G=(V,E)$ be any conflict graph. When using ColourResolve on $G$ with any proper graph colouring algorithm and any heuristic $h$, the resulting set of arguments $\Omega$ is always admissible.

Figures (11)

  • Figure 1: A conflict graph representation of the set of norms discussed in Section \ref{['sectionintroduction']}. The number inside each vertex corresponds to the number of the norm it represents. An edge connecting two vertices represents a conflict between the two corresponding norms.
  • Figure 2: An example of a graph with --- (a) A valid 5-colouring. (b) A valid 2-colouring. (c) An invalid 3-colouring.
  • Figure 3: (a) An uncoloured graph. (b) The same graph with a valid 3-colouring. (c) The same graph with an independent vertex set of size 3 (shown in grey).
  • Figure 4: A timeline showing which norms an agent should follow over time. In (a), we have norm $x_1$ which should be followed between 09:00 and 17:00, and a conflicting norm $y$ which should be followed between 12:00 and 13:00. Norm $x_2$ in (b) is the curtailed version of norm $x_1$ wrt. norm $y$, which does not conflict with $y.$
  • Figure 5: A diagram comparing the relative performances of algorithms proposed in Section \ref{['sectionnewstuff']}. An arrow from any algorithm $a$ to any algorithm $b$ represents that algorithm $a$ produces an equal or superior output to algorithm $b$.
  • ...and 6 more figures

Theorems & Definitions (6)

  • Proposition 1
  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Proposition 2