**Temporal Contextual Attention in Hierarchical Multi-Agent

Temporal Contextual Attention in Hierarchical Multi-Agent Systems with Non-Stationary Reward Functions

In this challenge, we task the community with developing an AI agent that can adapt to a complex environment with multiple stakeholders and non-stationary rewards.

Challenge Overview:

Consider a scenario with N hierarchical multi-agent systems, each comprising M agents, operating in a shared workspace. The agents are tasked with completing K distinct tasks, each with its own temporal context, non-stationary reward function, and multiple stakeholders.

Constraints:

1. The agents in each multi-agent system interact with a common knowledge graph, which evolves over time.
2. The reward functions for each task are non-stationary, meaning their parameters change according to a predefined, but unknown, probability distribution.
3. Each stakeholder has a unique reward function, which may prioritize different tasks or agents.
4. The temporal context of each task affects the agent's decision-making process, and its reward function is influenced by the actions of other agents in the system.
5. The agents must reason about the system's global state, taking into account the knowledge graphs, reward functions, and temporal contexts of all tasks and stakeholders.

Evaluation Metrics:

The proposed AI agent will be evaluated based on its ability to:

1. Maximize the cumulative reward across all tasks and stakeholders.
2. Adapt to changes in the reward functions, knowledge graphs, and temporal contexts.
3. Reason effectively about the system's global state and make decisions that balance individual and collective goals.

Dataset:

A synthetic dataset will be provided, comprising:

1. A set of task descriptions, including temporal contexts, reward functions, and knowledge graphs.
2. A set of agent interactions, including observations, actions, and rewards.
3. A set of stakeholder descriptions, including their reward functions and priorities.

Submission Guidelines:

Submissions should include a detailed description of the proposed AI agent architecture, including its reasoning mechanisms, knowledge representation, and decision-making process. Additionally, participants should provide an implementation of their agent in a publicly accessible repository, along with a clear set of instructions for reproducing the results.

Evaluation Criteria:

1. Performance on the evaluation metrics (cumulative reward, adaptability, and global reasoning).
2. Quality of the proposed architecture, including its scalability, robustness, and maintainability.
3. Clarity and concision of the submission, including documentation, code organization, and testing.

The best submission will receive a prestigious award and become a benchmark for future AI research in hierarchical multi-agent systems.

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