Bambon Negotiator

Bambon (Bayesian Adaptive Mixed-type Bilateral ODRL-policy Negotiator) is a learning-based negotiator for negotiating constraints over one or multiple actions (e.g., use, read, share, transfer) expressed in an ODRL-style policy format.

Bambon observes opponent offers, updates Bayesian beliefs about opponent preferences (numeric / enum / set issues), and generates counter-offers until:

• agreement is reached (odrl:Agreement), or
• the negotiation becomes infeasible (NegotiationInfeasible)

Features

• ✅ Negotiates constraints per action (single-action or multi-action offers)
• ✅ Supports mixed-type issues:
  • numeric constraints
  • categorical/enum constraints
  • set-valued constraints
• ✅ Bayesian belief models to learn opponent preferences over time
• ✅ Hard policy enforcement (non-negotiable constraints)
• ✅ Context similarity & severity scoring
• ✅ Hopelessness detection (fails fast if negotiation diverges)

Repository Structure

• negotiator.py
  Main implementation: Bambon (observe / counter-offer / agreement logic)

• belief.py
BeliefRegistry manages belief models per action & issue

• distributions.py
  Bayesian belief distributions:

  • numeric beta-based model
  • Dirichlet model for categorical constraints
  • set belief handling

• models.py
  Core dataclasses and NegotiationInfeasible exception

• helpers.py
  Constraint parsing + operator helpers

• context.py
  Similarity + severity scoring

• expressions.py
  Expression helpers for constraints

• test_negotiator.py
  Negotiation simulations / profiles (balanced, aggressive, exploratory)

Offer Format (ODRL-style)

Bambon expects offers in an ODRL-like JSON format:

{
  "@id": "urn:uid:example",
  "@type": "odrl:Offer",
  "permission": [
    {
      "action": "use",
      "constraint": [
        {
          "leftOperand": "odrl:purpose",
          "operator": "odrl:eq",
          "rightOperand": "research"
        }
      ]
    }
  ]
}

Constraints are normalized internally (e.g., odrl: prefixes stripped during parsing).

Quickstart

1) Create a negotiator

from negotiator import Bambon

neg = Bambon(
    name="Provider",
    init_offer=init_offer,        # required
    hard_policy=hard_policy       # optional
)

Each action in init_offer["permission"] gets its own:

• belief registry
• negotiation state

2) Observe an opponent offer and generate a response

signals, response_offer = neg.observe(opponent_offer)

print("Signals:", signals)
print("Response:", response_offer)

• signals is None while negotiating
• signals becomes non-null when an action is accepted and agreement is formed
• response_offer will be either:
  • a counter-offer (odrl:Offer), or
  • an agreement (odrl:Agreement)

Running

Run tests (recommended)

pytest -q

or:

python -m unittest -v

Example Minimal Negotiation Loop

from negotiator import Bambon

provider = Bambon(name="Provider", init_offer=provider_init, hard_policy=provider_hard)
consumer = Bambon(name="Consumer", init_offer=consumer_init)

offer = consumer_init
for round_i in range(50):
    sig_p, offer = provider.observe(offer)
    if offer.get("@type") == "odrl:Agreement":
        print("Agreement reached (provider -> consumer)")
        break

    sig_c, offer = consumer.observe(offer)
    if offer.get("@type") == "odrl:Agreement":
        print("Agreement reached (consumer -> provider)")
        break
else:
    print("No agreement reached within max rounds")

Key Negotiation Parameters

Bambon behavior is governed by tunable controls, including:

• lr — learning rate for belief updates
• inertia — resistance to changing past proposals
• tau_in, tau_out — thresholds controlling adaptation vs acceptance
• max_set_k — maximum size for proposed sets
• jaccard_snap_threshold — snaps set proposals when overlap is high
• no_agreement_until — prevents early agreement before minimum rounds
• max_rounds — maximum rounds before infeasibility
• hopeless_window, hopeless_patience — detects non-converging negotiation

Negotiation terminates with NegotiationInfeasible when:

• maximum number of rounds is exceeded, or
• persistent low context and high severity indicate divergence

This enables early stopping in non-converging negotiations.

Negotiation Semantics

Bambon operates under incomplete and asymmetric information, modeling opponent preferences through Bayesian belief updates.

Each issue is internally represented in a computational domain (numeric, categorical, or set-based), enabling:

• probabilistic acceptance decisions using Monte Carlo sampling
• operator-aware constraint compatibility checks
• adaptive proposal generation based on learned beliefs

Acceptance is not deterministic:

• numeric issues use credible intervals and probabilistic consistency
• categorical issues use Dirichlet-based argmax likelihood
• set issues use Beta-Bernoulli inclusion probabilities

Global negotiation signals:

• context score (overall similarity of offers)
• severity (degree of disagreement)

These signals influence acceptance thresholds dynamically.

Multi-Action Negotiation

Each action (e.g., use, read, share) is negotiated independently:

• separate belief models per action
• separate convergence tracking
• combined into a single ODRL offer

An agreement is reached when all issues for at least one action are accepted.

Output Types

Bambon produces ODRL-like structures:

• Counter-offer: @type = "odrl:Offer"
• Agreement: @type = "odrl:Agreement" with constraints filled using accepted values

Acknowledgments

This work has been supported by the Horizon Europe research and innovation programme under the project CLIMRES (Grant Agreement No. 101147777).

The content of this paper reflects only the authors’ views, and the European Commission is not responsible for any use that may be made of the information it contains.

Experiments (ODRL 2026 Paper)

This repository accompanies the paper:

“Constrained Adaptive Negotiation Agent of ODRL Offers Under Incomplete and Asymmetric Information”
2nd ODRL and Beyond: Practical Applications and Challenges for Policy-Based Access and Usage Control
May 10–11, 2026, Dubrovnik, Croatia

Running Experiments

To reproduce the experimental results:

1. Run negotiation simulations:

python test_negotiator.py

2. Then open and execute the notebook: results_analysis.ipynb The notebook performs the analysis and visualization of the negotiation outcomes.