negmas-geniusweb-bridge 0.1.2

A bridge that allows you to run GeniusWeb agents in negmas SAOMechanism(s).

negmas-geniusweb-bridge

A bridge that allows you to run GeniusWeb negotiation agents within NegMAS mechanisms.

Official GeniusWeb Resources

This project builds upon the official GeniusWeb framework. For the original implementations:

• GeniusWeb (Java): https://gitlab.ewi.tudelft.nl/interactive-intelligence/geniusweb/geniusweb
• GeniusWeb (Python): https://gitlab.ewi.tudelft.nl/interactive-intelligence/geniusweb/geniuswebpython
• GeniusWeb Project Home: https://gitlab.ewi.tudelft.nl/interactive-intelligence/geniusweb

The official repositories contain:

• Complete GeniusWeb framework implementation (Java and Python)
• Competition agents from ANAC 2020-2023
• Documentation and examples

AI-Assisted Development Disclaimer

Important Notice: Parts of this project were developed with AI assistance:

• GeniusWeb-to-NegMAS Wrapper (wrapper.py): The bridge wrapper that enables GeniusWeb agents to run in NegMAS mechanisms was developed in part using AI assistance.
• Java-to-Python Translations: The negotiation agents in the anac2020/ and anac2021/ directories were translated from their original Java implementations to Python using AI. These translations aim to preserve the original algorithms and strategies but may contain differences from the original implementations.

The original Python agents (in basic/, anl2022/, anl2023/, cse3210/) were written directly in Python by their original authors and are not AI-translated.

Users should be aware that AI-translated code may require additional validation for research or production use.

Installation

uv sync

For local development with a local negmas installation:

uv sync && uv pip install -e ../negmas

Usage

Basic Example

Run a negotiation between a GeniusWeb agent and a NegMAS agent:

from negmas import SAOMechanism, make_issue
from negmas.preferences import LinearAdditiveUtilityFunction
from negmas.sao import AspirationNegotiator

from negmas_geniusweb_bridge import BoulwareAgent

# Define the negotiation issues
issues = [make_issue(5, "price"), make_issue(3, "quality")]

# Create utility functions for each agent
ufun_a = LinearAdditiveUtilityFunction.random(issues=issues, normalized=True)
ufun_b = LinearAdditiveUtilityFunction.random(issues=issues, normalized=True)

# Create the negotiation mechanism
mechanism = SAOMechanism(issues=issues, n_steps=50)

# Create a GeniusWeb agent (Boulware strategy)
gw_agent = BoulwareAgent(ufun=ufun_a, name="geniusweb_boulware")

# Create a NegMAS agent (Aspiration strategy)
negmas_agent = AspirationNegotiator(ufun=ufun_b, name="aspiration")

# Add agents to the mechanism
mechanism.add(gw_agent)
mechanism.add(negmas_agent)

# Run the negotiation
mechanism.run()

# Check results
state = mechanism.state
print(f"Agreement: {state.agreement}")
print(f"Steps: {state.step}")

GeniusWeb vs GeniusWeb

Run a negotiation between two GeniusWeb agents:

from negmas import SAOMechanism, make_issue
from negmas.preferences import LinearAdditiveUtilityFunction

from negmas_geniusweb_bridge import BoulwareAgent, ConcederAgent

issues = [make_issue(10, "price"), make_issue(5, "quality"), make_issue(3, "delivery")]

ufun_a = LinearAdditiveUtilityFunction.random(issues=issues, normalized=True)
ufun_b = LinearAdditiveUtilityFunction.random(issues=issues, normalized=True)

mechanism = SAOMechanism(issues=issues, n_steps=100)

# Boulware agent (reluctant to concede)
agent_a = BoulwareAgent(ufun=ufun_a, name="boulware")

# Conceder agent (willing to concede quickly)
agent_b = ConcederAgent(ufun=ufun_b, name="conceder")

mechanism.add(agent_a)
mechanism.add(agent_b)
mechanism.run()

print(f"Agreement: {mechanism.state.agreement}")

Using the Factory Function

Create reusable negotiator classes:

from negmas_geniusweb_bridge.wrapper import make_geniusweb_negotiator
from negmas_geniusweb_bridge.basic.boulware_agent.boulware_agent import BoulwareAgent

# Create a reusable negotiator class
BoulwareNegotiator = make_geniusweb_negotiator(BoulwareAgent)

# Use it like any other NegMAS negotiator
negotiator = BoulwareNegotiator(ufun=my_ufun, name="boulware1")

Available Agents

The bridge includes 82+ agents from several GeniusWeb competitions. Each module exports:

• AGENTS: Dictionary of raw GeniusWeb party classes
• WRAPPED_AGENTS: Dictionary of NegMAS-wrapped negotiator classes (GW-prefixed)
• AGENT_NOTES: Known issues/notes about specific agents

Agent Summary

Module	Count	Type	Description
basic	7	Python Native	Reference implementations (Boulware, Conceder, Linear, etc.)
anac2020	13	AI-Translated	ANAC 2020 competition agents (from Java)
anac2021	6	AI-Translated	ANAC 2021 competition agents (from Java)
anl2022	18	Python Native	Automated Negotiation League 2022
anl2023	14	Python Native	Automated Negotiation League 2023
cse3210	25	Python Native	TU Delft CSE3210 course agents
Total	82

Basic Agents (7)

Reference implementations of classic negotiation strategies.

Agent	Description	Notes
BoulwareAgent	Time-dependent concession (hardliner early, concedes late)
ConcederAgent	Time-dependent concession (concedes early)
LinearAgent	Linear concession over time
HardlinerAgent	Never concedes	Only useful for testing
RandomAgent	Random bid selection
StupidAgent	Simple random behavior	Test agent
TimeDependentAgent	Base class for time-dependent strategies	Configurable via e parameter

ANAC 2020 Agents (13) - AI-Translated from Java

Agents from the Automated Negotiating Agents Competition 2020. These were translated from Java using AI assistance.

Agent	Protocol	Description
AgentKT	SHAOP/SAOP	COBYLA optimization with game-theoretic thresholds
AgentP1DAMO	SHAOP	Hill climbing with importance maps
AgentXX	SHAOP/SAOP	Importance maps with Nash point estimation
AhBuNeAgent	SHAOP	Similarity-based bidding with elicitation
Anaconda	SHAOP	Dynamic lower bounds with elicitation
Angel	SHAOP/SAOP	Heuristic opponent modeling with elicitation
AzarAgent	SHAOP/SAOP	GravityEs user model with frequency modeling
BlingBling	SHAOP/SAOP	RankNet neural network for preference learning
DUOAgent	SHAOP/SAOP	Linear regression for bid prediction
ForArisa	SAOP	Genetic algorithm for utility estimation
HammingAgent	SAOP	Hamming distance for opponent modeling
NiceAgent	SHAOP/SAOP	Elicitation with mirroring strategy
ShineAgent	SAOP	Adaptive agent with dynamic strategy

ANAC 2021 Agents (6) - AI-Translated from Java

Agents from the Automated Negotiating Agents Competition 2021. These were translated from Java using AI assistance.

Agent	Protocol	Description
AgentFO2021	SAOP	Learning-based agent with time-dependent concession
AlphaBIU	SAOP	Frequency-based opponent modeling with two-phase strategy
GamblerAgent	SAOP	UCB Multi-Armed Bandit selecting among sub-agents
MatrixAlienAgent	SAOP	Adaptive boulware-style with multi-factor bid scoring
TheDiceHaggler2021	SAOP	Multi-phase strategy with Pareto estimation and TOPSIS
TripleAgent	SAOP	Frequency model and utility space analysis

ANL 2022 Agents (18) - Python Native

Agents from the Automated Negotiation League 2022. Written in Python by their original authors.

Agent	Notes
Agent007
Agent4410
AgentFish
AgentFO2
BIUAgent	May timeout >60 secs on some domains
ChargingBoul
CompromisingAgent	May cause "Action cannot be None" errors
DreamTeam109Agent
GEAAgent	Slow execution (~1.5sec per turn)
LearningAgent	May cause "Action cannot be None" errors
LuckyAgent2022
MiCROAgent
PinarAgent	Requires lightgbm package (optional)
ProcrastinAgent	May have issues with first offer accepted
RGAgent
SmartAgent
SuperAgent
ThirdAgent
Tjaronchery10Agent

ANL 2023 Agents (14) - Python Native

Agents from the Automated Negotiation League 2023. Written in Python by their original authors.

Agent	Notes
AgentFO3
AmbitiousAgent
AntAllianceAgent
AntHeartAgent
ColmanAnacondotAgent2
ExploitAgent
GotAgent
HybridAgent2023
KBTimeDiffAgent
MiCRO2023
MSCAgent	Requires gym, torch, stable-baselines3 (optional)
PopularAgent
SmartAgent
SpaghettiAgent
TripleEAgent

CSE3210 Agents (25) - Python Native

Agents from the TU Delft CSE3210 Negotiation course. Written in Python by students.

Agent	Notes
Agent2 - Agent68	25 agents total
Agent22	May throw scipy divide by zero errors
Agent68	May have issues handling opening bid

Accessing Agents Programmatically

# Import all agents from a module
from negmas_geniusweb_bridge.anac2020 import AGENTS, WRAPPED_AGENTS, AGENT_METADATA
from negmas_geniusweb_bridge.anac2021 import AGENTS, WRAPPED_AGENTS, AGENT_NOTES
from negmas_geniusweb_bridge.anl2022 import AGENTS, WRAPPED_AGENTS, AGENT_NOTES
from negmas_geniusweb_bridge.anl2023 import AGENTS, WRAPPED_AGENTS
from negmas_geniusweb_bridge.cse3210 import AGENTS, WRAPPED_AGENTS
from negmas_geniusweb_bridge.basic import AGENTS, WRAPPED_AGENTS

# Import all wrapped agents at once
from negmas_geniusweb_bridge import ALL_AGENTS

# List all available agents
print(list(ALL_AGENTS.keys()))

# Use a wrapped agent directly
from negmas_geniusweb_bridge import HammingAgent, BoulwareAgent
agent = HammingAgent(name="my_agent")

Testing

# Run all tests
python -m pytest

# Run a specific test
python -m pytest tests/test_negmas_wrapper.py::TestNegotiationRuns::test_geniusweb_vs_geniusweb -v

License

This software is licensed for academic, research, and non-commercial use only.

Commercial use requires a separate license. Contact the author for commercial licensing inquiries.

See LICENSE for full terms.