Blue Abstract line waves.

 Benchmarking and Evaluating AI for Modeling and Simulation (BEAMS) 

The mission of the BEAMS Initiative is to engage the AI and modeling communities in devising tests for how well artificial intelligence and machine learning tools support the modeling process, so as to foster the development of more responsible and ethical tools.

BEAMS is a new initiative of the Institute for Artificial Intelligence and Data Science (IAD) at the ÃÛÌÒ´«Ã½ (UB). The BEAMS Initiative extends beyond UB and welcomes industry professionals as well as academics.

Initiative Lead:

Mission

The mission of the BEAMS Initiative is to engage the AI and modeling communities in devising tests for how well artificial intelligence and machine learning tools support the modeling process, so as to foster the development of more responsible and ethical tools.

Resources

Triangles, squares, parallelograms, circles, and diamonds are connected by arrows denoted + and - to represent positive and negative causal relationships. Each chain of arrows completes a feedback loop. A diagram with purple shapes hovers over a diagram with blue shapes.

Comparison of two model structures that may be generated from AI tools. Geometric shapes represent concepts linked by positive or negative causal relationships. Both structures create balancing feedback through similar but distinct mechanisms, and the bottom structure includes additional components.

Motivation

AI-enabled tools in the simulation and modeling community are actively developing and many have already been released to wide swaths of practitioners. There are currently no generalizable guidelines for evaluating these tools’ ability to advance the state of the art in modeling and simulation, either in terms of correctness (i.e., do the tools produce coherent thinking?) or usefulness (i.e., regardless of whether or not the tool functions, is this a useful tool for solving problems?). The BEAMS Initiative brings together people who build these tools (e.g., researchers, software engineers and data scientists) as well as people who use these tools (e.g., educators, professionals and modelers) to establish benchmarks that allow us to confirm not only technical correctness but also to chart a pathway towards usefulness.

Organization

The BEAMS Initiative includes two working groups: a steering group and a technical group. The steering group advises and oversees the direction of the BEAMS Initiative, including developing the theory underlying the benchmarks along with prioritizing the benchmarks to be implemented. The technical group focuses on implementing the vision laid out by the steering group in the form of unit tests for the sd-ai project at .

Meetings

Meetings of all collaborators in the BEAMS Initiative are held on an annual basis, while meetings of the technical and steering groups are held monthly (see ). All working group meetings are open to all collaborators in the BEAMS Initiative.

Contact Dr. Metcalf, for more information:

smetcalf@buffalo.edu

Initiative Collaborators:

Andrei Borshchev, The AnyLogic Company

Sindy Campagna, PwC

Karim Chichakly, isee systems, inc. 

Andrew Crooks, ÃÛÌÒ´«Ã½

Warren Farr, DynamicVu

Navid Ghaffarzadegan, Virginia Tech

Philippe Giabbanelli, VMASC, Old Dominion ÃÛÌÒ´«Ã½

Rachael Hageman Blair, ÃÛÌÒ´«Ã½

Niyousha Hosseinichimeh, Virginia Tech

Yingjie Hu, ÃÛÌÒ´«Ã½

Mohammad Jalali, Harvard ÃÛÌÒ´«Ã½

Tony Kennedy, Ventana Systems, Inc.

Birgit Kopainsky, ÃÛÌÒ´«Ã½ of Bergen

Aritra Majumdar, Virginia Tech

Donald Martin, Jr., The Global Institute for the Learning Society

Asmeret Naugle, Sandia National Laboratories

Ellen O’Neill, Washington ÃÛÌÒ´«Ã½ in St. Louis

Pia Ramchandani, PwC

Peter Rogerson, Arizona State ÃÛÌÒ´«Ã½

William Schoenberg, isee systems, inc. and ÃÛÌÒ´«Ã½ of Bergen

Hannah Shapiro, PwC

Rodrigo Villarreal Pérez, PwC

Lyle Wallis, Decisio