Verification of neural-symbolic agent-based systems

Considerable work has been carried out in the past two decades on
Verification of Multi-Agent Systems. Various methods based on
binary-decision diagrams, bounded model checking, abstraction,
symmetry reduction have been developed. Model checkers such as MCMAS
and various extensions have been released to the community and applied
to various areas including autonomous vehicles, services, robotics and

While this work has reached a level of maturity it is based on the
assumption that agents can be entirely modelled symbolically, e.g. via
a BDI rule system. This was entirely appropriate at the time. A new
model of agents, however, is rapidly emerging in applications. In this
model, agents are composed by a perception layer (vision, sound
processing, etc.) which is realised via neural networks, combined with
a decision making and planning layer realised via more traditional
declarative methods. We call this emerging class of agents
“neural-symbolic agents”.

This project concerns the development of methods and tools for the
verification of neural agents composed as such. The starting point
will be [1] where a neural-symbolic agent-based model is
developed. Several possible objectives can be targetted, including:

* The development of efficient (ie scalable) methods for verifying
neural-symbolic agent-based systems via novel compilations and

* The development of a verification toolkit for the verification of
neural-symbolic agents,

* The development of highly parallel approaches for the verification
of neural-symbolic agents.


M. Akintunde, E. Botoeva, P. Kouvaros, A. Lomuscio. Verifying Strategic Abilities of Neural Multi-agent Systems. Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR20). Rhodes, Greece. IJCAI Press.

For previous papers in this line, see papers by the same authors, including AAMAS2020, and KR2018 from


Expertise with Logic, and Verification will be highly beneficial. Some knowledge of machine learning and optimisation is also advantageous.

Project ID