**Temporal Contextual Attention in Hierarchical Multi-Agent

Source: Dev.to

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

• Agents in each system interact with a common knowledge graph that evolves over time.
• Reward functions for each task are non‑stationary; their parameters change according to a predefined, but unknown, probability distribution.
• Each stakeholder has a unique reward function, potentially prioritizing different tasks or agents.
• The temporal context of each task influences decision‑making, and its reward function is affected by actions of other agents.
• Agents must reason about the global state, taking into account knowledge graphs, reward functions, and temporal contexts of all tasks and stakeholders.

Evaluation Metrics

The AI agent will be evaluated on its ability to:

1. Maximize cumulative reward across all tasks and stakeholders.
2. Adapt to changes in 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, containing:

• Task descriptions: temporal contexts, reward functions, and knowledge graphs.
• Agent interactions: observations, actions, and rewards.
• Stakeholder descriptions: reward functions and priorities.

Submission Guidelines

Submissions should include:

• A detailed description of the proposed AI agent architecture, covering reasoning mechanisms, knowledge representation, and decision‑making process.
• An implementation hosted in a publicly accessible repository.
• Clear instructions for reproducing the results.

Evaluation Criteria

• Performance on the evaluation metrics (cumulative reward, adaptability, global reasoning).
• Quality of architecture: scalability, robustness, and maintainability.
• Clarity and concision of the submission: 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.