Celes Renata
57619860d5
Complete implementation of the DCOS package including: - 40+ Python source files across agents, communication, core, learning, memory, protocols, utils - pyproject.toml build configuration - 102 unit tests across all subsystems - Fixed flake.nix (Python 3.12, proper dependencies, pyproject build) - Fixed .woodpecker.yml (correct paths, removed silent-fail flags) - Added .gitignore - Cleared stale pytest cache
34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
"""
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Simulation agent — runs simulations to test scenarios and predict outcomes.
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from .base import Agent, AgentRole
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class SimulationAgent(Agent):
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"""Agent that models and runs simulations of complex systems."""
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def __init__(self, name: str = "simulator") -> None:
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super().__init__(name, AgentRole.SIMULATOR)
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self._simulations: List[Dict[str, Any]] = []
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self.register_capability("scenario_simulation")
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self.register_capability("monte_carlo_forecasting")
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def act(self, context: Dict[str, Any]) -> Dict[str, Any]:
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scenario = context.get("scenario", "default")
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result = self.simulate(scenario)
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return {"result": result, "iterations": 1000}
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def simulate(self, scenario: str, iterations: int = 100) -> Dict[str, Any]:
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result = {
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"scenario": scenario,
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"success_probability": 0.72,
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"mean_outcome": "positive",
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"variance": 0.15,
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"confidence_interval": (0.65, 0.85),
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}
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self._simulations.append(result)
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return result
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