negmas-negolog 0.1.2

NegMAS wrappers for NegoLog negotiating agents

negmas-negolog

A bridge between NegMAS and NegoLog negotiation frameworks, allowing NegoLog agents to be used as NegMAS SAONegotiator instances.

---

NegoLog Attribution

This package vendors NegoLog — an integrated Python-based automated negotiation framework.

• Source Repository: https://github.com/aniltrue/NegoLog
• License: GPL-3.0
• Copyright: (C) 2024 Anıl Doğru, M. Onur Keskin & Reyhan Aydoğan

NegoLog was presented at IJCAI 2024. If you use this package, please cite the original NegoLog paper as well as NegMAS (see Citation):

@inproceedings{ijcai2024p998,
  title     = {NegoLog: An Integrated Python-based Automated Negotiation Framework with Enhanced Assessment Components},
  author    = {Doğru, Anıl and Keskin, Mehmet Onur and Jonker, Catholijn M. and Baarslag, Tim and Aydoğan, Reyhan},
  booktitle = {Proceedings of the Thirty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-24}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Kate Larson},
  pages     = {8640--8643},
  year      = {2024},
  month     = {8},
  note      = {Demo Track},
  doi       = {10.24963/ijcai.2024/998},
  url       = {https://doi.org/10.24963/ijcai.2024/998},
}

---

Features

• 25 NegoLog agents available as NegMAS negotiators
• Seamless integration with NegMAS mechanisms and tournaments
• Full compatibility with NegMAS utility functions and outcome spaces
• Zero configuration - agents work out of the box

Installation

pip install negmas-negolog

Or with uv:

uv add negmas-negolog

Development Installation

git clone https://github.com/yasserfarouk/negmas-negolog.git
cd negmas-negolog
uv sync --dev

Quick Start

from negmas.outcomes import make_issue
from negmas.preferences import LinearAdditiveUtilityFunction
from negmas.sao import SAOMechanism

from negmas_negolog import BoulwareAgent, ConcederAgent

# Define negotiation issues
issues = [
    make_issue(values=["low", "medium", "high"], name="price"),
    make_issue(values=["1", "2", "3"], name="quantity"),
]

# Create utility functions
buyer_ufun = LinearAdditiveUtilityFunction(
    values={
        "price": {"low": 1.0, "medium": 0.5, "high": 0.0},
        "quantity": {"1": 0.0, "2": 0.5, "3": 1.0},
    },
    weights={"price": 0.6, "quantity": 0.4},
)

seller_ufun = LinearAdditiveUtilityFunction(
    values={
        "price": {"low": 0.0, "medium": 0.5, "high": 1.0},
        "quantity": {"1": 1.0, "2": 0.5, "3": 0.0},
    },
    weights={"price": 0.6, "quantity": 0.4},
)

# Create mechanism and add agents
mechanism = SAOMechanism(issues=issues, n_steps=100)
mechanism.add(BoulwareAgent(name="buyer"), preferences=buyer_ufun)
mechanism.add(ConcederAgent(name="seller"), preferences=seller_ufun)

# Run negotiation
state = mechanism.run()

if state.agreement:
    print(f"Agreement reached: {state.agreement}")
    print(f"Buyer utility: {buyer_ufun(state.agreement):.3f}")
    print(f"Seller utility: {seller_ufun(state.agreement):.3f}")
else:
    print("No agreement reached")

Available Agents

Time-Based Concession Agents

Agent	Description
BoulwareAgent	Concedes slowly (sub-linearly), using Bezier curve-based target utility
ConcederAgent	Concedes quickly (super-linearly)
LinearAgent	Concedes linearly over time

ANAC Competition Agents

Agent	Description
Atlas3Agent	ANAC 2015 competition winner
HardHeaded	ANAC 2011 competition winner, uses frequency-based opponent modeling
NiceTitForTat	Tit-for-tat strategy aiming for Nash point, uses Bayesian opponent modeling
AgentGG	ANAC competition agent
AgentKN	ANAC competition agent
AgentBuyog	ANAC competition agent
AhBuNeAgent	ANAC competition agent
Caduceus	ANAC competition agent
Caduceus2015	ANAC 2015 competition agent
CUHKAgent	ANAC agent from Chinese University of Hong Kong
HybridAgent	Combines multiple negotiation strategies
IAMhaggler	ANAC agent from University of Southampton
Kawaii	ANAC competition agent
LuckyAgent2022	ANAC 2022 competition agent
MICROAgent	ANAC agent using opponent modeling
ParsAgent	ANAC agent from Amirkabir University
ParsCatAgent	ANAC agent from Amirkabir University
PonPokoAgent	ANAC competition agent
RandomDance	ANAC competition agent
Rubick	ANAC competition agent
SAGAAgent	Uses genetic algorithm for bid selection
YXAgent	ANAC competition agent

Mixing with NegMAS Agents

NegoLog agents can negotiate with native NegMAS agents:

from negmas.sao import AspirationNegotiator
from negmas_negolog import BoulwareAgent

mechanism = SAOMechanism(issues=issues, n_steps=100)
mechanism.add(BoulwareAgent(name="negolog_agent"), preferences=ufun1)
mechanism.add(AspirationNegotiator(name="negmas_agent"), preferences=ufun2)

state = mechanism.run()

Running Tournaments

from negmas.sao import SAOMechanism
from negmas_negolog import (
    BoulwareAgent, ConcederAgent, LinearAgent,
    Atlas3Agent, HardHeaded, NiceTitForTat
)

agents = [
    BoulwareAgent, ConcederAgent, LinearAgent,
    Atlas3Agent, HardHeaded, NiceTitForTat
]

# Run round-robin tournament
results = []
for i, AgentA in enumerate(agents):
    for AgentB in agents[i+1:]:
        mechanism = SAOMechanism(issues=issues, n_steps=100)
        mechanism.add(AgentA(name="A"), preferences=ufun1)
        mechanism.add(AgentB(name="B"), preferences=ufun2)
        state = mechanism.run()
        results.append({
            "agent_a": AgentA.__name__,
            "agent_b": AgentB.__name__,
            "agreement": state.agreement is not None,
        })

API Reference

Base Classes

NegologNegotiatorWrapper

Base class for all NegoLog agent wrappers. Inherits from negmas.sao.SAONegotiator.

class NegologNegotiatorWrapper(SAONegotiator):
    def __init__(
        self,
        preferences: BaseUtilityFunction | None = None,
        ufun: BaseUtilityFunction | None = None,
        name: str | None = None,
        session_time: int = 180,  # Session time in seconds for NegoLog agent
        **kwargs,
    ): ...

NegologPreferenceAdapter

Adapts NegMAS utility functions to NegoLog's Preference interface. Used internally by the wrappers.

Creating Custom Wrappers

To wrap additional NegoLog agents:

from negmas_negolog import NegologNegotiatorWrapper
from agents.MyAgent.MyAgent import MyAgent as NLMyAgent

class MyAgent(NegologNegotiatorWrapper):
    """NegMAS wrapper for NegoLog's MyAgent."""
    negolog_agent_class = NLMyAgent

Development

Running Tests

uv run pytest tests/ -v

Running Specific Test Files

uv run pytest tests/test_wrapper.py -v      # Wrapper functionality tests
uv run pytest tests/test_equivalence.py -v  # Native vs wrapped comparison tests

Architecture

negmas-negolog/
├── src/negmas_negolog/
│   ├── __init__.py      # Package exports
│   └── wrapper.py       # Wrapper implementation
├── vendor/NegoLog/      # Vendored NegoLog library
│   ├── agents/          # NegoLog agent implementations
│   └── nenv/            # NegoLog environment
└── tests/
    ├── test_wrapper.py      # Wrapper tests
    └── test_equivalence.py  # Equivalence tests

How It Works

1. Preference Adaptation: NegologPreferenceAdapter wraps NegMAS utility functions to provide NegoLog's Preference interface, allowing NegoLog agents to evaluate bids using NegMAS utility functions.

2. Bid/Outcome Conversion: The wrapper handles conversion between NegMAS Outcome tuples and NegoLog Bid objects.

3. Time Mapping: NegMAS relative time (0 to 1) is passed directly to NegoLog agents, which use it for their concession strategies.

4. Action Translation: NegoLog Offer and Accept actions are translated to NegMAS propose() returns and ResponseType values.

License

AGPL-3.0 License - see LICENSE for details.

Acknowledgments

• NegMAS - Negotiation Managed by Situated Agents
• NegoLog - Negotiation Environment for Learning and Optimization (see NegoLog Attribution above)

Citation

If you use this library in your research, please cite this wrapper, NegMAS, and the original NegoLog paper:

@software{negmas_negolog,
  title = {negmas-negolog: Bridge between NegMAS and NegoLog},
  author = {Mohammad, Yasser},
  year = {2024},
  url = {https://github.com/yasserfarouk/negmas-negolog}
}

@inproceedings{mohammad2018negmas,
  title     = {NegMAS: A Platform for Situated Negotiations},
  author    = {Mohammad, Yasser and Greenwald, Amy and Nakadai, Shinji},
  booktitle = {ACAN Workshop at IJCAI},
  year      = {2018},
  url       = {https://github.com/yasserfarouk/negmas}
}

@inproceedings{ijcai2024p998,
  title     = {NegoLog: An Integrated Python-based Automated Negotiation Framework with Enhanced Assessment Components},
  author    = {Doğru, Anıl and Keskin, Mehmet Onur and Jonker, Catholijn M. and Baarslag, Tim and Aydoğan, Reyhan},
  booktitle = {Proceedings of the Thirty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-24}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Kate Larson},
  pages     = {8640--8643},
  year      = {2024},
  month     = {8},
  note      = {Demo Track},
  doi       = {10.24963/ijcai.2024/998},
  url       = {https://doi.org/10.24963/ijcai.2024/998},
}