Partially Controlled Multi-Agent Systems (PCMAS) are comprised of controllable agents, managed by a system designer, and uncontrollable agents, operating autonomously. This study addresses an optimal composition design problem in PCMAS, which involves the system designer's problem, determining the optimal number and policies of controllable agents, and the uncontrollable agents' problem, identifying their best-response policies. Solving this bi-level optimization problem is computationally intensive, as it requires repeatedly solving multi-agent reinforcement learning problems under various compositions for both types of agents. To address these challenges, we propose a novel hypernetwork-based framework that jointly optimizes the system's composition and agent policies. Unlike traditional methods that train separate policy networks for each composition, the proposed framework generates policies for both controllable and uncontrollable agents through a unified hypernetwork. This approach enables efficient information sharing across similar configurations, thereby reducing computational overhead. Additional improvements are achieved by incorporating reward parameter optimization and mean action networks. Using real-world New York City taxi data, we demonstrate that our framework outperforms existing methods in approximating equilibrium policies. Our experimental results show significant improvements in key performance metrics, such as order response rate and served demand, highlighting the practical utility of controlling agents and their potential to enhance decision-making in PCMAS.

Thanks to popular science fiction, almost any person on Earth has some knowledge of AI. For business purposes and utilizing advanced technologies there are deeper reasons to look into Artificial Intelligence. Saving time and money, increasing productivity and revenues, avoiding human factor errors is what you get from AI right off the top of a hat. Hundreds of artificial intelligence companies are already conquering markets. The main purpose of our article is not reviewing pros and cons, rather offering AI development companies which can assist your business strategy for consideration. On one hand, our list does not include big-guns like Amazon, Alexa or Apple, and on the other, we also discarded plenty of startups with questionable reputation. We handpicked only those tech companies who, in our opinion, are able to cope with the most challenging AI projects and have positive customer feedback.

The commoditisation of corporate legal services has been going on for some time. Since 2007, in-house legal teams have been squeezed for resources. Law firms are under increased cost pressures, made worse by increased competition from within and outside the legal sector. Because of the inelastic nature of legal spend on high-value bespoke projects, many legal activities regarded as low value-add, such as bulk contract analysis, are passed on to low-cost providers. Data in the US has demonstrated price erosion in the legal industry, with declining revenue per lawyer that may only be offset by cost reduction and an increase in non-equity partners.

I have been asked many times by clients to provide fixed price estimates for large Machine Learning (ML) projects. I advise clients not to fight requirements changes in their first ML project, which is the exact opposite from traditional software development principles. Machine learning is not regular programming. It is basically applied data science, and rolls out very differently in an organization that already has a non-machine learning infrastructure as compared to a start-up with a clean slate. The bigger the unknown, or what I like to call requirements risk, the more I lean toward hourly rather than fixed price. Where requirements are very tight, or what I like to call an executable specification, the more I lean towards fixed pricing.