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Michael Akintunde

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3 papers

2 author rows

AAMAS Conference 2024 Conference Paper

Actual Trust in Multiagent Systems

Michael Akintunde
Vahid Yazdanpanah
Asieh Salehi Fathabadi
Corina Cirstea
Mehdi Dastani
Luc Moreau

We study how trust can be established in multiagent systems where human and AI agents collaborate. We propose a computational notion of actual trust, emphasising the modelling of trust based on agents’ capacity to deliver tasks in prospect. Unlike reputationbased trust, we consider the specific setting in which agents interact and model a forward-looking notion of trust. We provide a conceptual analysis of actual trust’s characteristics and highlight relevant trust verification tools. By advancing the understanding and verification of trust in collaborative systems, we contribute to responsible and trustworthy human-AI interactions, enhancing reliability in various domains.

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NeSy Conference 2023 Conference Paper

Verifying Strategic Abilities of Neural-Symbolic Multi-agent Systems

Michael Akintunde
Elena Botoeva
Panagiotis Kouvaros
Alessio Lomuscio

We investigate the problem of verifying the strategic properties of multi-agent systems equipped with machine learning-based perception units. We introduce a novel model of agents comprising both a perception system implemented via feed-forward neural networks and an action selection mechanism implemented via traditional control logic. We define the verification problem for these systems against a bounded fragment of alternating-time temporal logic. We translate the verification problem on bounded traces into the feasibility problem of mixed integer linear programs and show the soundness and completeness of the translation. We show that the lower bound of the verification problem is PSPACE and the upper bound is coNEXPTIME. We present a tool implementing the compilation and evaluate the experimental results obtained on a complex scenario of multiple aircraft operating a recently proposed prototype for air-traffic collision avoidance. The full paper appeared in the Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR20). Rhodes, Greece. IJCAI Press. The full paper can be accessed at https: //proceedings. kr. org/2020/3/ The work was partly funded by DARPA under the Assured Autonomy programme (FA8750-18-C0095), the EPSRC Centre for Doctoral Training in High Performance Embedded and Distributed Systems (EP/L016796/1) and the Royal Academy of Engineering Chair in Emerging Technologies.

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KR Conference 2018 Conference Paper

Reachability Analysis for Neural Agent-Environment Systems

Michael Akintunde
Alessio Lomuscio
Lalit Maganti
Edoardo Pirovano

We develop a novel model for studying agent-environment systems, where the agents are implemented via feed-forward ReLU neural networks. We provide a semantics and develop a method to verify automatically that no unwanted states are reached by the system during its evolution. We study several reachability problems for the system, ranging from one-step reachability, to fixed multi-step and arbitrary-step to study the system evolution. We also study the decision problem of whether an agent, realised via feed-forward ReLU networks will perform an action in a system run. Whenever possible, we give tight complexity bounds to decision problems introduced. We automate the various reachability problems studied by recasting them as mixed-integer linear programming problems. We present an implementation and discuss the experimental results obtained on a range of test cases.