Presenting Agent Simulation Experiments in COGNISIM (available)

Starting Date: June 2021
Duration: 6 weeks
Time commitment: Full time
Prerequisites: Excellent Python programming, understanding of Prolog, acquaintance with Intelligent Agents/AI concepts, and an interest to explore game-theoretic evolutionary models.

When we simulate a system or a process, we normally want to imitate its operation over time. This need arises in a variety of contexts, from engineering to testing, training, education and video games. We are interested in simulation models that involve modeling human systems to gain an understanding of how they behave over time. To support this interest, we have developed in Prolog a platform called COGNISIM, which allows us to model human systems with cognitive agents.

In the current form the COGNISIM platform lacks a usable interface to present the results of simulation experiments. To address this limitation, this UROP project aims to develop such an interface to support a developer specify simulation experiments, link them with the functionality of intelligent agents, and analyze their results. The specific objective of the project is to explore how to develop a front-end for COGNISIM using Python and specifically the Python-Prolog bridge known as PySwip. Through this bridge we expect to explore the usefulness of other Python visualization libraries such as Pandas, Matplotlib, Plotly, or FusionBrew to better present the results of simulation experiments.

As this proposal results from experimenting with evolutionary models of cooperation in game theory, the successful candidate will need to use existing models of this type to test the work. The successful candidate will also participate in group meetings within the DICE Lab at RHUL, organised by Prof. Kostas Stathis and Ms Nausheen Saba Shahid (Ph.D. Candidate), who will co-supervise the work.

We anticipate that this project will help the successful candidate gain useful insights of game-theoretic simulations using cognitive agents, as well as improve their programming skills in the thematic areas of the project.

Further reading:

D. Weyns and F. Michel, “Agent environments for multi-agent systems –a research roadmap,” in Agent Environments for Multi-Agent Systems IV (D. Weyns and F. Michel, eds.), (Cham), pp. 3–21, Springer International Publishing, 2015.

S. Bromuri and K. Stathis, “Situating cognitive agents in Golem” in Engineering environment-mediated multi-agent systems, pp. 115–134, Springer, 2008.

M. Seki and M. Nakamaru, “A model for gossip-mediated evolution of altruism with various types of false information by speakers and assessment by listeners,”Journal of Theoretical Biology, vol. 407, pp. 90–105, 2016.

“Pyswip v0.2.9” 2020. https://pypi.org/project/pyswip/. Last accessed 1 March, 2021.

P. Sharma, “11 python data visualization libraries data scientists should know,” 2021. Last accessed 1 March, 2021.