We experimented with various transformer-based language models, such as BERT [9], RoBERTa [10], and T5 [11] for these datasets to identify the most effective model based on our evaluation. After splitting the restructured datasets into a 90-10 ratio for training and testing, we proceeded to train the selected models (block highlighted in yellow) using a grid search of hyperparameters optimization. Given the multitude of possible tactics available at each proof state, we chose to provide multiple recommendations for the next proof step. To assess the accuracy of these recommendations (block highlighted in green), we use the n-correctness rate, which measures the likelihood that a correct tactic from the testing dataset is among the top-n recommended tactics, where n signifies the number of recommended tactics evaluated against the correct tactic. We found out that RoBERTa demonstrated superior performance across most cases for n = 7.

It has been shown that social institutions impact human motivations to produce different behaviours, such as amount of working or specialisation in labor. With advancement in artificial intelligence (AI), specifically large language models (LLMs), now it is possible to perform in-silico simulations to test various hypotheses around this topic. Here, I simulate two somewhat similar worlds using multi-agent reinforcement learning (MARL) framework of the AI-Economist and generative agent-based model (GABM) framework of the Concordia. In the extended versions of the AI-Economist and Concordia, the agents are able to build houses, trade houses, and trade house building skill. Moreover, along the individualistic-collectivists axis, there are a set of three governing systems: Full-Libertarian, Semi-Libertarian/Utilitarian, and Full-Utilitarian. Additionally, in the extended AI-Economist, the Semi-Libertarian/Utilitarian system is further divided to a set of three governing institutions along the discriminative axis: Inclusive, Arbitrary, and Extractive. Building on these, I am able to show that among governing systems and institutions of the extended AI-Economist, under the Semi-Libertarian/Utilitarian and Inclusive government, the ratios of building houses to trading houses and trading house building skill are higher than the rest. Furthermore, I am able to show that in the extended Concordia when the central government care about equality in the society, the Full-Utilitarian system generates agents building more houses and trading more house building skill. In contrast, these economic activities are higher under the Full-Libertarian system when the central government cares about productivity in the society. Overall, the focus of this paper is to compare and contrast two advanced techniques of AI, MARL and GABM, to simulate a similar social phenomena with limitations.

In an era where artificial intelligence (AI) is reshaping countless fields, the research community of social sciences needs to adapt to the changes posed by these technologies [1, 2]. In particular, data quality and authenticity play a significant role in social sciences [3], where the conclusions drawn rely heavily on data collected, for instance, from surveys. There are many traditional ways of gathering data, such as public datasets or private surveys, but AI has led to innovative approaches, like using agent-based models (ABMs). In recent years, the use of this paradigm has gained significant attention across a variety of fields, from economics and social sciences to artificial intelligence and computational biology [4, 5, 6]. ABMs allow researchers to simulate complex situations by modeling the behaviors and interactions of individual agents within a given environment [7]. These models provide a powerful way to understand emergent phenomena--such as market dynamics, social behaviors, or ecological systems--that arise from the independent actions and interactions of individual agents, each following its own set of rules. In spite of their flexibility, these models face some limitations, particularly when dealing with complex environments. One of the main challenges is that the agents' behaviors are programmed by the modeler based on assumptions or simplified rules. This rigid structure limits the ability to account for the full range of possible interactions that can emerge in real-world scenarios.

The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-world settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present a simulation environment designed to address this. We elaborate upon the Concordia framework that simulates offline, 'real life' activity by adding online interactions to the simulation through social media with the integration of a Mastodon server. We improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys. We demonstrate the simulator with a tailored example in which we track agents' political positions and show how partisan manipulation of agents can affect election results.

Agent-based modeling has been around for decades, and applied widely across the social and natural sciences. The scope of this research method is now poised to grow dramatically as it absorbs the new affordances provided by Large Language Models (LLM)s. Generative Agent-Based Models (GABM) are not just classic Agent-Based Models (ABM)s where the agents talk to one another. Rather, GABMs are constructed using an LLM to apply common sense to situations, act "reasonably", recall common semantic knowledge, produce API calls to control digital technologies like apps, and communicate both within the simulation and to researchers viewing it from the outside. Here we present Concordia, a library to facilitate constructing and working with GABMs. Concordia makes it easy to construct language-mediated simulations of physically- or digitally-grounded environments. Concordia agents produce their behavior using a flexible component system which mediates between two fundamental operations: LLM calls and associative memory retrieval. A special agent called the Game Master (GM), which was inspired by tabletop role-playing games, is responsible for simulating the environment where the agents interact. Agents take actions by describing what they want to do in natural language. The GM then translates their actions into appropriate implementations. In a simulated physical world, the GM checks the physical plausibility of agent actions and describes their effects. In digital environments simulating technologies such as apps and services, the GM may handle API calls to integrate with external tools such as general AI assistants (e.g., Bard, ChatGPT), and digital apps (e.g., Calendar, Email, Search, etc.). Concordia was designed to support a wide array of applications both in scientific research and for evaluating performance of real digital services by simulating users and/or generating synthetic data.

An alternative to stratified is parallel integration. In contrast to stratified frameworks, parallel integration applies in settings Parallel neurosymbolic architectures have been in which the same task can be solved both symbolically applied effectively in NLP by distilling knowledge and sub-symbolically and the aim is to increase the accuracy from a logic theory into a deep model. However, of the end task by distilling knowledge from the logic prior art faces several limitations including component into the neural one and vice versa. Two parallel supporting restricted forms of logic theories and neurosymbolic frameworks have been proposed recently: relying on the assumption of independence between Teacher-Student (T-S) by Hu et al. (Hu et al., 2016a;b) and the logic and the deep network.

Artificial intelligence is a new and upcoming form of technology that performs in a way the human brain does, in order to make the lives of humans quicker, easier, and even more accurate. Machines have been created to think and analyze the same as the human brain. These machines can now complete tasks such as analyzing data in a variety of areas, perform security and surveillance coverage, and overall being able to improve existing processes. Computer programs have been given past information and results of human behavior in order to formulate into the intelligence of humans. Beginning in approximately the 1950's, the idea of intelligent machines was being created when a mathematician, Alan Turing, released his article on how to create and test the intelligence of a machine. As this was the start of a long lasting revolutionary concept, the term artificial intelligence didn't come about until a few years later when John McCarthy coined the term'artificial intelligence.' The first artificial intelligence program created is very controversial as around the same decade many scientists were working towards creating different programs that each had different capabilities. One of the first programs created was a program with the ability to "mimic the problem solving skills of a human," and was called the Logic Theorist.

The need for improvement has been changing the world for thousands of years. Numerous inventions have filled gaps with intended and unintended uses in response to this desire for advancement. Take the wheel, for example. It was developed to assist potters with their clay back in Mesopotamia around 3500 B.C. (Gambino, 2009). Now, we get thousands of different uses out of the wheel.

Negin Ashouri is on a mission to elevate women's quality of life, one medical device at a time. Even the challenges of a global pandemic haven't stopped the up-and-coming entrepreneur from advancing a first-of-its-kind technology that is enabling her to do just that. Ashouri's made-to-measure, biodegradable and disposable intravaginal prosthetic for women suffering from pelvic organ prolapse has earned her a prestigious award from Mitacs. Ashouri was presented the Mitacs Change Agent Entrepreneur Award at a virtual awards ceremony on June 10. She was one of five Mitacs Entrepreneur Award winners recognized for their efforts to turn their research into an innovative business that impacts the lives of Canadians.

Artificial intelligence, or AI, has the potential to cause harm but can it also make the world a better place? Much of the concern has to do with AI's capacity to displace vast segments of the workforce, increase unemployment and broaden the wealth divide. However, there's also a lot of excitement around the burgeoning technology's myriad possible applications for tackling issues related to education, poverty, health and climate change, to name a few. Montreal has established itself as a hub for AI research and development, but much of the work up until now has been focused on solving business challenges -- not taking on major societal or environmental problems. Xavier-Henri Hervé is executive director of the District 3 Innovation Center at Concordia.