Search Results for author:
Found 27 papers, 4 papers with code
The Reasons that Agents Act: Intention and Instrumental Goals
In addition, we show how our definition relates to past concepts, including actual causality, and the notion of instrumental goals, which is a core idea in the literature on safe AI agents.
Leveraging Approximate Model-based Shielding for Probabilistic Safety Guarantees in Continuous Environments
Shielding is a popular technique for achieving safe reinforcement learning (RL).
Stability of Multi-Agent Learning in Competitive Networks: Delaying the Onset of Chaos
Motivated by this we study the Q-Learning dynamics, a popular model of exploration and exploitation in multi-agent learning, in competitive network games.
Honesty Is the Best Policy: Defining and Mitigating AI Deception
There are a number of existing definitions of deception in the literature on game theory and symbolic AI, but there is no overarching theory of deception for learning agents in games.
3vLTL: A Tool to Generate Automata for Three-valued LTL
Multi-valued logics have a long tradition in the literature on system verification, including run-time verification.
Approximate Model-Based Shielding for Safe Reinforcement Learning
Reinforcement learning (RL) has shown great potential for solving complex tasks in a variety of domains.
Stability of Multi-Agent Learning: Convergence in Network Games with Many Players
The behaviour of multi-agent learning in many player games has been shown to display complex dynamics outside of restrictive examples such as network zero-sum games.
Characterising Decision Theories with Mechanised Causal Graphs
How should my own decisions affect my beliefs about the outcomes I expect to achieve?
Approximate Shielding of Atari Agents for Safe Exploration
Balancing exploration and conservatism in the constrained setting is an important problem if we are to use reinforcement learning for meaningful tasks in the real world.
Asymptotic Convergence and Performance of Multi-Agent Q-Learning Dynamics
Achieving convergence of multiple learning agents in general $N$-player games is imperative for the development of safe and reliable machine learning (ML) algorithms and their application to autonomous systems.
Argumentative Reward Learning: Reasoning About Human Preferences
We define a novel neuro-symbolic framework, argumentative reward learning, which combines preference-based argumentation with existing approaches to reinforcement learning from human feedback.
A Sahlqvist-style Correspondence Theorem for Linear-time Temporal Logic
The main result of this paper is to prove the correspondence of LTL Sahlqvist formulas to frame conditions that are definable in first-order language.
Model Checking Strategic Abilities in Information-sharing Systems
To underline, the fragment of ATL for which we show the model-checking problem to be decidable over A-cast is a large and significant one; it expresses coalitions over agents in any subset of the set A.
Reasoning about Human-Friendly Strategies in Repeated Keyword Auctions
In online advertising, search engines sell ad placements for keywords continuously through auctions.
Do Androids Dream of Electric Fences? Safety-Aware Reinforcement Learning with Latent Shielding
The growing trend of fledgling reinforcement learning systems making their way into real-world applications has been accompanied by growing concerns for their safety and robustness.
In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications
We address the problem of building agents whose goal is to learn to execute out-of distribution (OOD) multi-task instructions expressed in temporal logic (TL) by using deep reinforcement learning (DRL).
Aggregating Bipolar Opinions (With Appendix)
We introduce a novel method to aggregate Bipolar Argumentation (BA) Frameworks expressing opinions by different parties in debates.
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors
Multi-agent reinforcement learning (RL) often struggles to ensure the safe behaviours of the learning agents, and therefore it is generally not adapted to safety-critical applications.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning
This work introduces a neuro-symbolic agent that combines deep reinforcement learning (DRL) with temporal logic (TL) to achieve systematic zero-shot, i. e., never-seen-before, generalisation of formally specified instructions.
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement Learning
The combination of Formal Methods with Reinforcement Learning (RL) has recently attracted interest as a way for single-agent RL to learn multiple-task specifications.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Formal Verification of Debates in Argumentation Theory
Such transition systems can model debates and represent their evolution over time using a finite set of states.
Social Choice Methods for Database Aggregation
Knowledge can be represented compactly in multiple ways, from a set of propositional formulas, to a Kripke model, to a database.
Database Aggregation
Knowledge can be represented compactly in a multitude ways, from a set of propositional formulas, to a Kripke model, to a database.
Databases
A Logic for Global and Local Announcements
In this paper we introduce {\em global and local announcement logic} (GLAL), a dynamic epistemic logic with two distinct announcement operators -- $[\phi]^+_A$ and $[\phi]^-_A$ indexed to a subset $A$ of the set $Ag$ of all agents -- for global and local announcements respectively.
Relaxing Exclusive Control in Boolean Games
We introduce Concurrent Game Structures with Shared Propositional Control (CGS-SPC) and show that they ac- count for several classes of repeated games, including iterated boolean games, influence games, and aggregation games.
Reasoning about Knowledge and Strategies: Epistemic Strategy Logic
In this paper we introduce Epistemic Strategy Logic (ESL), an extension of Strategy Logic with modal operators for individual knowledge.
Interactions between Knowledge and Time in a First-Order Logic for Multi-Agent Systems: Completeness Results
We investigate a class of first-order temporal-epistemic logics for reasoning about multi-agent systems.
