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Found 27 papers, 5 papers with code
Code Simulation Challenges for Large Language Models
We investigate the extent to which Large Language Models (LLMs) can simulate the execution of computer code and algorithms.
Interventionally Consistent Surrogates for Agent-based Simulators
Agent-based simulators provide granular representations of complex intelligent systems by directly modelling the interactions of the system's constituent agents.
Language Models as a Service: Overview of a New Paradigm and its Challenges
This paper has two goals: on the one hand, we delineate how the aforementioned challenges act as impediments to the accessibility, replicability, reliability, and trustworthiness of LMaaS.
Cognitive Effects in Large Language Models
Large Language Models (LLMs) such as ChatGPT have received enormous attention over the past year and are now used by hundreds of millions of people every day.
On Imperfect Recall in Multi-Agent Influence Diagrams
Multi-agent influence diagrams (MAIDs) are a popular game-theoretic model based on Bayesian networks.
Some challenges of calibrating differentiable agent-based models
Agent-based models (ABMs) are a promising approach to modelling and reasoning about complex systems, yet their application in practice is impeded by their complexity, discrete nature, and the difficulty of performing parameter inference and optimisation tasks.
Bayesian calibration of differentiable agent-based models
Agent-based modelling (ABMing) is a powerful and intuitive approach to modelling complex systems; however, the intractability of ABMs' likelihood functions and the non-differentiability of the mathematical operations comprising these models present a challenge to their use in the real world.
Reasoning about Causality in Games
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.
Ten Years after ImageNet: A 360° Perspective on AI
The rise of attention networks, self-supervised learning, generative modeling, and graph neural networks has widened the application space of AI.
Learning Task Automata for Reinforcement Learning using Hidden Markov Models
Second, we propose a novel method for distilling the task automaton (assumed to be a deterministic finite automaton) from the learnt product MDP.
On the Complexity of Rational Verification
To this end, we consider both utilitarian and egalitarian social welfare and show that computing such outcomes is either PSPACE-complete or NP-complete.
Multiagent Model-based Credit Assignment for Continuous Control
By formulating robotic components as a system of decentralised agents, this work presents a decentralised multiagent reinforcement learning framework for continuous control.
Rational Verification for Probabilistic Systems
Rational verification is the problem of determining which temporal logic properties will hold in a multi-agent system, under the assumption that agents in the system act rationally, by choosing strategies that collectively form a game-theoretic equilibrium.
Equilibrium Design for Concurrent Games
In particular, we consider system specifications given by LTL and GR(1) formulae, and show that implementing a mechanism to ensure that a given temporal logic property is satisfied on some/every Nash equilibrium of the game, whenever such a mechanism exists, can be done in PSPACE for LTL properties and in NP/$\Sigma^{P}_{2}$ for GR(1) specifications.
Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and Practice
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.
Multi-Agent Reinforcement Learning with Temporal Logic Specifications
In this paper, we study the problem of learning to satisfy temporal logic specifications with a group of agents in an unknown environment, which may exhibit probabilistic behaviour.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Multi-Player Games with LDL Goals over Finite Traces
Linear Dynamic Logic on finite traces LDLf is a powerful logic for reasoning about the behaviour of concurrent and multi-agent systems.
Equilibria for Games with Combined Qualitative and Quantitative Objectives
The overall aim of our research is to develop techniques to reason about the equilibrium properties of multi-agent systems.
Automated Temporal Equilibrium Analysis: Verification and Synthesis of Multi-Player Games
In the context of multi-agent systems, the rational verification problem is concerned with checking which temporal logic properties will hold in a system when its constituent agents are assumed to behave rationally and strategically in pursuit of individual objectives.
Replication-Robust Payoff-Allocation for Machine Learning Data Markets
In this paper, we systematically study the replication manipulation in submodular games and investigate replication robustness, a metric that quantitatively measures the robustness of solution concepts against replication.
Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination
We evaluate our model empirically on a set of multi-agent pursuit and taxi tasks, and show that our agents learn to adapt flexibly across scenarios that require different termination behaviours.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Manipulating a Learning Defender and Ways to Counteract
We then apply a game-theoretic framework at a higher level to counteract such manipulation, in which the defender commits to a policy that specifies her strategy commitment according to the learned information.
Game-theoretic Network Centrality: A Review
Game-theoretic centrality is a flexible and sophisticated approach to identify the most important nodes in a network.
Hiding Individuals and Communities in a Social Network
With this in mind, we ask the question: Can individuals or groups actively manage their connections to evade social network analysis tools?
Social and Information Networks Physics and Society 91D30 (Primary) 68T20 (Secondary) G.2.2; J.4
Boolean Hedonic Games
The assumption of dichotomous preferences means that, additionally, each player's preference relation partitions the set of coalitions of which that player is a member into just two equivalence classes: satisfactory and unsatisfactory.
Reasoning About the Transfer of Control
The logical foundation of DCL-PC is CL-PC, a logic for reasoning about cooperation in which the abilities of agents and coalitions of agents stem from a distribution of atomic Boolean variables to individual agents -- the choices available to a coalition correspond to assignments to the variables the coalition controls.
A Linear Approximation Method for the Shapley Value
This method has time complexity linear in the number of players, but has an approximation error that is, on average, lower than Owen's.
