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concordia_contest_neurips

Agent description

This agent structure is specifically designed to handle complex social interactions within the Concordia framework, with a strong emphasis on negotiation, reputation management, and strategic decision-making while maintaining a memory of past interactions and observations.

The code follows a modular design pattern where each component handles a specific aspect of the agent's functionality, all orchestrated through the main build_agent function. This design allows for flexibility in agent behavior while maintaining a structured approach to decision-making and interaction.

Build Function Structure:

The main function build_agent() takes several key parameters:

config: Agent configuration model: Language model interface memory: Associative memory system clock: Time management system update_time_interval: Timing for agent updates

Core Agent Components:

Instructions: Handles basic agent directives Time Display: Manages temporal awareness Observation System:

Current Observations Observation Summary (24-hour lookback) Relevant Memories (retrieves 10 most relevant memories)

Specialized Negotiation Components:

a) Paranoia/Truth Component:

Contains the agent's core negotiation strategies including:

Situation analysis Rapport building Tactical empathy Question techniques Conflict management Multi-party negotiation handling Impasse management Continuous learning

b) Person Representation:

Tracks other agents' behaviors Evaluates if behaviors match expected patterns Checks for potential imposters

c) Reputation System:

Monitors trustworthiness of other agents Tracks history of cooperative behavior Maintains reputation assessments

Decision Making Components:

a) Options Perception:

Analyzes available choices Integrates observations and memories Considers reputation data

b) Best Option Perception:

Evaluates optimal actions Considers goals and context Integrates multiple information streams

Component Organization:

Uses ordered component structure Maintains clear separation of concerns Allows for optional components (like goals)

Logging System:

Comprehensive measurement tracking Component-specific logging channels Integrated with main agent actions

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