negmas was designed mainly to support multi-strand multilateral multi-issue negotiations with complex utility functions. This section gives an introduction to the main concepts of the public interface.

In order to use the library you will need to import it as follows (assuming that you followed the instructions in the installation section of this document):

import negmas


The package is organized into a set of modules/packages that combine together related functionality. There are base modules, protocol specific modules, advanced and helper modules.

  • Base Modules Implements basic automated negotiation functionality:

    1. outcomes This module represents issues, outcome and responses and provides basic functions and methods to operator with and on them.

    2. utilities This modules represents the base type of all utilities and different widely used utility function types including linear and nonlinear utilities and constraint-based utilities. This module also implements basic analysis tools like finding the pareto-frontier, sampling outcomes with given utilities from the outcome space, etc.

    3. negotiators This module represents basic negotiation agent implementation and provides basic interfaces to be overriden (implemented) by higher specialized modules

    4. mechanisms This module represents the most basic conceptual view of a negotiation protocol supporting both mediate and unmediated mechanisms. The term mechanism was used instead of the more common protocol to stress the fact that this mechanism need not be a standard negotiation protocol. For example auction mechanisms (like second-price auctions) can easily be implemented as a Mechanism in negmas.

    5. common Provides data structures that are used by all modules including mechanism-state, and the agent-mechanism-interface.

    6. genius Implements a specific type negotiator for the stacked alternating offers protocol called GeniusNegotiator which can run NegotiationParty based agents from the Java Genius platform.

  • Mechanism Specific Modules These modules implement the base mechanism, negotiator type(s), state, and related computational resources specific to a single (or a set of related) negotiation/auction protocols

    1. sao Implements that stacked alternating offers protocol for unmediated multiparty multi-issue negotiations. Other than providing the SAOMechanism class representing the protocol, this package provides a set of simple negotiators including the time-based AspirationNegotiator, a SimpleTitForTatNegotiator, among others.

    2. st Implements two basic single-text mediated negotiation protocols (veto and hill-climbing) and the basic negotiator types to support them.

    3. mt Implements and extension of single text mediated protocols to handle multiple proposed agreements in parallel.

    4. ga Implements a Genetic Algorithm based single text mediated negotiation protocol

  • Advanced Negotiation Modules These modules model advanced negotiation problems and techniques

    1. situated Implements world simulations within which agents with intrinsic utility functions can engage in simultaneous interconnected situated negotiations. It is the most important module for the goals of this library. The Agent and World classes described in details later belong to this module

    2. modeling This is a set of submodules implementing modeling of opponent utility, opponent strategy, opponent’s future offers and opponent’s probability of accepting offers.

    3. elicitation Implements several preference elicitation during negotiation methods.

    4. concurrent Implements mechanism types, and other computational resources to support concurrent negotiation.

  • Helper Modules These modules provide basic activities that is not directly related to the negotiation but that are relied upon by different base modules. The end user is not expected to interact directly with these modules.

    • common Provides common interfaces that are used by all other modules.

    • generics Provides a set of types and interfaces to increase the representation flexibility of different base modules.

    • helpers Various helper functions and classes used throughout the library including mixins for logging.

    • inout Provides functions to load and store XML Genius domains and utility functions.

    • java [Depricated] Provides an interface to JNegMAS allowing agents and negotiators to be developed in Java.

    • tournaments Supports creating and running tournaments to compare agents and negotiators.

    • checkpoints Supports saving and reloading world simulations to/from secondary storage.

    • visualizers [Under development] Supports visualization of world simulation, negotiation sessions, negotiators, and agents.

To simplify the use of this platform, all classes and functions from all base modules are aliased in the root package (except generics and helpers). This is an example of importing just Outcome which is defined in the outcomes package

from negmas import Outcome

It is possible to just import everything in the package using:

from negmas import *

As usual you can just import everything in a separate namespace using:

import negmas

Negotiations are conducted between multiple agents with the goal of achieving an agreement (usually called a contract) on one of several possible outcomes. Each outcome is in general an assignment to some value to a set of issues. Each issue is a variable that can take one of a – probably infinite – set of values from some predefined domain.

The classes and functions supporting management of issues, outcomes and responses are combined in the outcomes module.


Issues are represented in negmas using the issue class. An issue is defined by a set of values and a name. It can be created as follows:

  • Using a set of strings:

# an issue with randomly assigned name
issue1 = Issue(values=['to be', 'not to be'])
# an issue with given name:
issue2 = Issue(values=['to be', 'not to be'], name='The Problem')
QKqgvpMitAUvhKCf: ['to be', 'not to be']
The Problem: ['to be', 'not to be']
  • Using a single integer to give an issue which takes any value from 0 to the given integer minus 1:

issue3 = Issue(values=10, name='number of items')
number of items: (0, 9)
  • Using a tuple with a lower and upper real-valued boundaries to give an issue with an infinite number of possibilities (all real numbers in between)

issue4 = Issue(values=(0.0, 1.0), name='cost')
cost: (0.0, 1.0)

The Issue class provides some useful functions. For example you can find the cardinality of any issue using:

[issue2.cardinality, issue3.cardinality, issue4.cardinality]
[2, 10, inf]

It is also possible to check the type of the issue and whether it is discrete or continuous:

[issue2.type, issue2.is_discrete(), issue2.is_continuous()]
['discrete', True, False]

It is possible to check the total cardinality for a set of issues:

[Issue.num_outcomes([issue1, issue2, issue3, issue4]), # expected inf
 Issue.num_outcomes([issue1, issue2, issue3])] # expected 40 = 2 * 2 * 10
[inf, 40]

You can pick random valid or invalid values for the issue:

    [issue1.rand_valid(), issue1.rand_invalid()],
    [issue3.rand_valid(), issue3.rand_invalid()],
    [issue4.rand_valid(), issue4.rand_invalid()],
[['to be', '20210405H095721457952jJTGt6qBto be20210405H095721458380XtRgNy0I'],
 [6, 10],
 [0.6118970848141451, 1.928063278403899]]

You can also list all valid values for an issue using all or sample from them using alli. Notice that all and alli return generators so both are memory efficient.

except ValueError as e:
('to be', 'not to be')
('to be', 'not to be')
(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
Cannot return all possibilities of a continuous/uncountable issue


Now that we know how to define issues, defining outcomes from a negotiation is even simpler. An outcome can be any python mapping or iterable with a known length. That includes dictionaries, lists, tuples among many other.

Here is how to define an outcome for the last three issues mentioned above:

valid_outcome = {'The Problem': 'to be', 'number of items': 5, 'cost': 0.15}
invalid_outcome = {'The Problem': 'to be', 'number of items': 10, 'cost': 0.15}

Notice that the invalid_outcome is assigning a value of 10 to the number of items issue which is not an acceptable value (cost ranges between 0 and 9).

Because outcomes can be represented with many built-in collection classes, the only common ancestor of all outcome objects is the object class. Nevertheless, the outcomes module provide a type-alias Outcome that can be used for static type checking if needed. The outcomes module also provides some functions for dealing with outcome objects in relation to Issues. These are some examples:

    outcome_is_valid(valid_outcome, [issue2, issue3, issue4]),      # valid giving True
    outcome_is_valid(invalid_outcome, [issue2, issue3, issue4])     # invalid giving False
[True, False]

It is not necessary for an outcome to assign a value for all issues to be considered valid. For example the following outcomes are all valid for the last three issues given above:

    outcome_is_valid({'The Problem': 'to be'}, [issue2, issue3, issue4]),
    outcome_is_valid({'The Problem': 'to be', 'number of items': 5}, [issue2, issue3, issue4])
[True, True]

You can check the validity of outcomes defined as tuples or lists the same way.

    outcome_is_valid(['to be', 4, 0.5], [issue2, issue3, issue4]),
    outcome_is_valid(('to be', 4, 1.5), [issue2, issue3, issue4])
[True, False]

It is also important for some applications to check if an outcome is complete in the sense that it assigns a valid value to every issue in the given set of issues. This can be done using the outcome_is_complete function:

    outcome_is_complete(valid_outcome, [issue2, issue3, issue4]),            # complete -> True
    outcome_is_complete(invalid_outcome, [issue2, issue3, issue4]),          # invalid -> incomplete -> False
    outcome_is_complete({'The Problem': 'to be'}, [issue2, issue3, issue4])  # incomplete -> False
[True, False, False]

It is sometimes difficult to keep track of issue names in dictionaries. For this reason, the library provides a type called OutcomeType. Inheriting your dataclass from an OutcomeType will allow it to act both as a dict and a normal dot accessible object:

from dataclasses import dataclass
class MyOutcome(OutcomeType):
    problem: bool
    price: float
    quantity: int

Now you can use objects of MyOutcome as normal outcomes

issues = [
    Issue(['to be', 'not to be'], name='problem'),
    Issue((0.0, 3.0), name='price'),
    Issue(5, name='quantity')
outcomes = Issue.sample(issues, n_outcomes = 5, astype=MyOutcome)
for _ in outcomes:
MyOutcome(problem='not to be', price=1.9292429442563561, quantity=0)
MyOutcome(problem='not to be', price=1.2168605028020283, quantity=4)
MyOutcome(problem='to be', price=0.5504030900242165, quantity=1)
MyOutcome(problem='to be', price=0.9922145014554281, quantity=2)
MyOutcome(problem='not to be', price=1.4245542660733643, quantity=1)

The sample function created objects of type MyOutcome that can be accessed using either the dot notation or as a dict

print(outcomes[0].get('price', None))

OutcomeType is intended to be used as a syntactic sugar around your outcome objects but it provides almost no functionality above a dict.

Outcome Ranges and constraints

Sometimes, it is important to represent not only a single outcome but a range of outcomes. This can be represented using an OutcomeRange. Again, an outcome range can be almost any mapping or iterable in python including dictionaries, lists, tuples, etc with the only exception that the values stored in it can be not only be int, str, float but also tuples of two of any of them representing a range or a list of values. This is easier shown:

range1 = {'The Problem': ['to be', 'not to be'], 'number of items': 5, 'cost': (0.1, 0.2)}

range1 represents the following range of outcomes:

  • The Problem: accepts both to be and not to be

  • number of items: accepts only the value 5

  • cost: accepts any real number between 0.1 and 0.2 up to representation error

It is easy to check whether a specific outcome is within a given range:

outcome1 = {'The Problem': 'to be', 'number of items': 5, 'cost': 0.15}
outcome2 = {'The Problem': 'to be', 'number of items': 10, 'cost': 0.15}
    outcome_in_range(outcome1, range1),       # True
    outcome_in_range(outcome2, range1)        # False
[True, False]

In general outcome ranges constraint outcomes depending on the type of the constraint:

  • tuple The outcome must fall within the range specified by the first and second elements. Only valid for values that can be compared using __lt__ (e.g. int, float, str).

  • single value The outcome must equal this given value.

  • list of values The outcome must be within the list.

  • list of tuples The outcome must fall within one of the ranges specified by the tuples.


Agents engage in negotiations to maximize their utility. That is the central dogma in negotiation research. negmas allows the user to define their own utility functions based on a set of predefined base classes that can be found in the utilities module.

Utility Values

In most applications, utility values can be represented by real numbers. Nevertheless, some applications need a more complicated representation. For example, during utility elicitation (the process of learning about the utility function of the human being represented by the agent) or opponent modeling (the process of learning about the utility function of an opponent), the need may arise to represent a probability distribution over utilities.

negmas allows all functions that receive a utility value to receive a utility distribution. This is achieved through the use of two basic type definitions:

  • UtilityDistribution That is a probability Distribution class capable of representing probabilistic variables having both continuous and discrete distributions and applying basic operations on them (addition, subtraction and multiplication). Currently we use scipy.stats for modeling these distributions but this is an implementation detail that should not be relied upon as it is likely that the probabilistic framework will be changed in the future to enhance the flexibility of the package and its integration with other probabilistic modeling packages (e.g. PyMC3).

  • UtilityValue This is the input and output type used whenever a utility value is to be represented in the whole package. It is defined as a union of a real value and a UtilityDistribution (Union[float, UtilityDistribution]). This way, it is possible to pass utility distributions to most functions expecting (or returning) a utility value including utility functions.

This means that both of the following are valid utility values

u1 = 1.0
u2 = UtilityDistribution(dtype='norm')   # standard normal distribution
norm(loc:0.0, scale:1.0)

Utility Functions

Utility functions are entities that take an Outcome and return its UtilityValue. There are many types of utility functions defined in the literature. In this package, the base of all utiliy functions is the UtilityFunction class which is defined in the utilities module. It behaves like a standard python Callable which can be called with a single Outcome object (i.e. a dictionary, list, tuple etc representing an outcome) and returns a UtilityValue. This allows utility functions to return a distribution instead of a single utility value.

Utility functions in negmas have a helper property called type which returns the type of the utility function and a helper function eu for returning the expected utility of a given outcome which is guaranteed to return a real number (float) even if the utiliy function itself is returning a utility distribution.

To implement a specific utility function, you need to override the single eval function provided in the UtilityFunction abstract interface. This is a simple example:

class ConstUtilityFunction(UtilityFunction):
   def eval(self, offer):
            return 3.0 * offer['cost']
        except KeyError:  # No value was given to the cost
            return None

   def xml(self):
        return '<ufun const=True value=3.0></ufun>'

f = ConstUtilityFunction()
[f({'The Problem': 'to be'}), f({'cost': 10})]
[None, 30.0]

Utility functions can store internal state and use it to return different values for the same outcome over time allowing for dynamic change or evolution of them during negotiations. For example this silly utility function responds to the mood of the user:

class MoodyUtilityFunction(UtilityFunction):
    def __init__(self, mood='good'):
        self.mood = mood

    def __call__(self, offer):
        return float(offer['cost']) if self.mood == 'good'\
                            else 0.1 * offer['cost'] if self.mood == 'bad' \
                            else None
    def set_mood(self, mood):
        self.mood = mood

    def xml(self):

offer = {'cost': 10.0}

f = MoodyUtilityFunction()
# I am in a good mode now
print(f'Utility in good mood of {offer} is {f(offer)}')
print(f'Utility in bad mood of {offer} is {f(offer)}')
print(f'Utility in good mood of {offer} is {f(offer)}')
Utility in good mood of {'cost': 10.0} is 10.0
Utility in bad mood of {'cost': 10.0} is 1.0
Utility in good mood of {'cost': 10.0} is None

Notice that (as the last example shows) utility functions can return None to indicate that the utility value cannot be inferred for this outcome/offer.

The package provides a set of predefined utility functions representing most widely used types. The following subsections describe them briefly:

Comparison Interface to UFuns

In some cases, the preferences that the negotiator is to work with are not directly given as a mapping from outcomes to utility values but as comparisons between different outcomes.

For example, we may have four outcomes A, B, C, D, and we know that each letter is better than the next except C and D that are equivalent. How can we encode this in negmas?

All preferences in negmas are encoded using a UtilityFunction so we must use one. Here is one example:

class MyUFun(UtilityFunction):
    def eval(self, outcome):
        raise ValueError("This ufun does not implement the direct evaluation interface")
    def xml(self):
        raise NotImplementedError('I do not know how to save myself to XML')
    def is_better(self, a, b):
        return a < b and not (a=='C' and b == 'D')

We can create a ufun of this type now as usual

u = MyUFun()

You can then use the ufun normally. In the future, the eval method will be approximated for those utility functions allowing them to be used directly in mechanisms that expect an outcome-value mapping. For now, they are only usable in mechanisms that rely on the is_better interface (e.g. the single-text mechanism STMechanism and its derivatives).

u.is_better('A', 'C')

Linear Aggregation Utility Functions

The LinearAggregationUtilityFunction class represents a function that linearly aggregate utilities assigned to issues in the given outcome which can be defined mathematically as follows:

\[U(o) = \sum_{i=0}^{\left|o\right|}{w_i\times g_i(o_i)}\]

where \(o\) is an outcome, \(w\) is a real-valued weight vector, \(\left|o\right|\) is the number of issues, \(o_i\) if the value assigned in outcome \(o\) to issue \(i\), and \(g\) is a vector of functions each mapping one issue of the outcome to some real-valued number (utility of this issue).

Notice that despite the name, this type of utiliy functions can represent nonlinear relation between issue values and utility values. The linearity is in how these possibly nonlinear mappings are being combind to generate a utility value for the outcome.

For example, the following utility function represents the utility of buyer who wants low cost, many items, and prefers delivery:

buyer_utility = LinearUtilityAggregationFunction({'price': lambda x: - x
                           , 'number of items': lambda x: 0.5 * x
                           , 'delivery': {'delivered': 1.0, 'not delivered': 0.0}})

Given this definition of utility, we can easily calculate the utility of different options:

print(buyer_utility({'price': 1.0, 'number of items': 3, 'delivery': 'not delivered'}))

Now what happens if we offer to deliver the items:

print(buyer_utility({'price': 1.0, 'number of items': 3, 'delivery': 'delivered'}))

And if delivery was accompanied with an increase in price

print(buyer_utility({'price': 1.8, 'number of items': 3, 'delivery': 'delivered'}))

It is clear that this buyer will still accept that increase of price from '1.0' to '1.8’ if it is accompanied with the delivery option.

Nonlinear Aggregation Utility Functions

A direct generalization of the linear agggregation utility functions is provided by the NonLinearAggregationUtilityFunction which represents the following function:

\[U(o) = f\left(\left\{{g_i(o_i)}\right\}\right)\]

where \(g\) is a vector of functions defined as before and \(f\) is a mapping from a vector of real-values to a single real value.

For example, a seller’s utility can be defined as:

seller_utility =NonLinearUtilityAggregationFunction({
                             'price': lambda x: x
                           , 'number of items': lambda x: 0.5 * x
                           , 'delivery': {'delivered': 1.0, 'not delivered': 0.0}}
                   , f=lambda x: x['price']/x['number of items'] - 0.5 * x['delivery'])

This utility will go up with the price and down with the number of items as expected but not linearly.

We can now evaluate different options similar to the case for the buyer:

print(seller_utility({'price': 1.0, 'number of items': 3, 'delivery': 'not delivered'}))
print(seller_utility({'price': 1.0, 'number of items': 3, 'delivery': 'delivered'}))
print(seller_utility({'price': 1.8, 'number of items': 3, 'delivery': 'delivered'}))

Hyper Rectangle Utility Functions

In many cases, it is not possible to define a utility mapping for every issue independently. We provide the utility function HyperVolumeUtilityFunction to handle this situation by allowing for representation of a set of nonlinear functions defined on arbitrary hyper-volumes of the space of outcomes.

The simplest example is a nonlinear-function that is defined over the whole space but that nonlinearly combines several issues to calculate the utility.

For example the previous NonLinearUtilityFunction for the seller can be represented as follows:

seller_utility = HyperRectangleUtilityFunction(
    outcome_ranges= [None],
    utilities= [
        lambda x: 2.0*x['price']/x['number of items']
        - 0.5 * int(x['delivery'] == 'delivered')
print(seller_utility({'price': 1.0, 'number of items': 3, 'delivery': 'not delivered'}))
print(seller_utility({'price': 1.0, 'number of items': 3, 'delivery': 'delivered'}))
print(seller_utility({'price': 1.8, 'number of items': 3, 'delivery': 'delivered'}))

This function recovered exactly the same values as the NonlinearUtilityFuction defined earlier by defining a single hyper-volume with the special value of None which applies the function to the whole space and then defining a single nonlinear function over the whole space to implement the required utiltiy mapping.

HyperVolumeUtilityFunction was designed to a more complex situation in which you can have multiple nonlinear functions defined over different parts of the space of possible outcomes.

Here is an example in which we combine one global utility function and two different local ones:

f = HyperRectangleUtilityFunction(
        {0: (1.0, 2.0), 1: (1.0, 2.0)},
        {0: (1.4, 2.0), 2: (2.0, 3.0)}
        5.0, 2.0, lambda x: 2 * x[2] + x[0]

There are three nonlinear functions in this example:

  • A global function which gives a utility of 5.0 everywhere

  • A local function which gives a utility of 2.0 to any outcome for which the first issue (issue 0) has a value between 1.0 and2.0and the second issue (issue1) has a value between1.0and2.0which is represented as:{0: (1.0, 2.0), 1: (1.0, 2.0)}``

  • A second local function which gives a utility that depends on both the third and first issues (lambda x: 2 * x[2] + x[0]) on the range {0: (1.4, 2.0), 2: (2.0, 3.0)}.

You can also have weights for combining these functions linearly. The default is just to sum all values from these functions to calculate the final utility.

Here are some examples: * An outcome that falls in the range of all constraints:

f([1.5, 1.5, 2.5])
  • An outcome that falls in the range of the global and first local constraints only:

f([1.5, 1.5, 1.0])
  • An outcome that misses a value for some of the issues:

print(f([1.5, 1.5]))

Notice that in this case, no utility is calculated because we do not know if the outcome falls within the range of the second local function or not. To allow such cases, the initializer of HyperVolumeUtilityFunction allows you to ignore such cases:

g = HyperRectangleUtilityFunction(
        {0: (1.0, 2.0), 1: (1.0, 2.0)},
        {0: (1.4, 2.0), 2: (2.0, 3.0)}
    utilities=[5.0, 2.0, lambda x: 2 * x[2] + x[0]],
print(g([1.5, 1.5]))

Nonlinear Hyper Rectangle Utility Functions

HyperVolumeUtilityFunction should be able to handle most complex multi-issue utility evaluations but we provide a more general class called NoneLinearHyperVolumeUtilityFunction which replaces the simple weighted summation of local/global functions implemented in HyperVolumeUtilityFunction with a more general nonlinar mapping.

The relation between NoneLinearHyperVolumeUtilityFunction and HyperVolumeUtilityFunction is exactly the same as that between NonLinearUtilityAggregationFunction and LinearUtilityAggregationFunction

Other utility function types

There are several other built-in utility function types in the utilities module. Operations for utility function serialization to and from xml as sell as normalization, finding pareto-frontier, generation of ufuns, etc are also available. Please check the documentation of the utilities module for more details

from pprint import pprint
pprint(list(_ for _ in negmas.utilities.__all__ if _.endswith("n")))

Utility Helpers and Analysis Tools

NegMAS provides a set of functions that help with common tasks required while developing negotiation agents. These are some examples:

  • pareto_frontier Finds the pareto-frontier of a set of utility functions.

  • make_discounted_ufun Takes a utility function and returns one that is discounted (linearly and/or exponentially).

  • normalize Normalizes a utility function within a given range.

  • outcome_with_utility Finds an outcome with a utility within some range.

  • utility_range Finds the range of values of a utility function and outcomes with highest and lowest utilities.


When negotiations are run, agents are allowed to respond to given offers for the final contract. An offer is simply an outcome (either complete or incomplete depending on the protocol but it is always valid). Negotiators can then respond with one of the values defined by the Response enumeration in the outcomes module. Currently these are:

  • ACCEPT_OFFER Accepts the offer.

  • REJECT_OFFER Rejects the offer.

  • END_NEGOTIATION This implies rejection of the offer and further more indicates that the agent is not willing to continue with the negotiation. The protocol is free to handle this situation. It may just end the negotiation with no agreement, may just remove the agent from the negotiation and keep it running with the remaining agents (if that makes sense) or just gives the agent a second chance by treating it as just a REJECT_OFFER case. In most case the first response (just end the negotiation) is expected.

  • NO_RESPONSE Making no response at all. This is usually not allowed by negotiation protocols and will be considered a protocol violation in most cases. Nevertheless, negotiation protocols are free to handle this response when it arise in any way.

  • WAIT Used to make the negotiation wait for a slow running process in one of the negotiators. This should never be returned from user code. It is used by some builtin controllers in the system to synchronize responses (e.g. SAOSyncController )

Rational Entities

A Rational entity in NegMAS is an object that has an associated UtilityFunction. There are three types of Rational entities defined in the library:

  • Negotiator represents a negotiation agent that can interact with Mechanism objects (representing negotiation protocols) using a dedicated AgentMechanismInterface the defines public information of the mechanism. A negotiator is tied to a single negotiation.

  • Agent represents a more complex entity than a negotiation agent. It does not interact directly with negotiation protocols (i.e. it does not have an AgentMechanismInterface) and is needed when there is a need to adjust behavior in multiple negotiations and/or when there is a need to interact with a simulation or the real world (represented in negmas by a World object) through an AgentWorldInterface.

  • Controller A mid-level entity between Negotiator and Agent. It can control multiple negotiator objects at the same time but it cannot interact with mechanisms or worlds directly. Usually controllers are created by agents to manage a set of interrelated negotiations through dedicated negotiators in each of them.


Negotiations are conducted by negotiators. We reserve the term Agent to more complex entities that can interact with a simulation or the real world and spawn Negotiator objects as needed (see the situated module documentation). The base Negotiator is implemented in the negotiators module. The design of this module tried to achieve maximum flexibility by relying mostly on Mixins instead of inheritance for adding functionality as will be described later.

To build your negotiator, you need to inherit from a Negotiator suitable for the negotiation mechanism your negotiator is compatible with, implement its abstract functions.

Negotiators related to a specific negotiation mechanism are implemented in that mechanism’s module. For example, negotiators designed for the Stacked Alternating Offers Mechanism are found in the sao module.

The Base Negotiator

The base class of all negotiators is Negotiator. Negotiators define callbacks that are called by Mechanisms to implement the negotiation protocol.

The base Negotiator class defines basic functionality including the ability to access the Mechanism settings in the form of an AgentMechanismInterface accessible through the ami attribute of the Negotiator.

Genius Negotiator

There is a special type of negotiators called GeniusNegotiator implemented in the genius module that is capable of interacting with negotiation sessions running in the genius platform (JVM). Please refer to the documentation of genius module for more information.


A Controller is an object that can control multiple negotiators either by taking full or partial control from the Negotiators. By default, controllers will just resend all requests received to the corresponding negotiator. This means that if you do not override any methods in the controller, all negotiation related actions will still be handled by the Negotiator. To allow controllers to actually manage negotiations, a subclass of Controller needs to implement these actions without calling the base class’s implementation.

A special kind of negotiator called PassThroughNegotiator is designed to work with controllers that take full responsibility of the negotiation. These negotiators act just as a relay station passing all requests from the mechanism object to the controller and all responses back.


Self interested entities in NegMAS can be represented by either Negotiators or Agents. Use negotiators when a single negotiation session is involved, otherwise use an agent. Agents can own both negotiators and controllers (that manage negotiators) and can act in the World (simulated or real).

Putting Everything together

Other than Rational objects, NegMAS defines two types of entities that orchestrate the interactions between Rational objects:

  • Mechanisms represent interaction protocols which can be negotiation protocols or auctions. A Mechanism object connects a set of Negotiators and implements the interaction protocol.

  • Worlds represent either the real world or (usually) a simulation that connects Agents together. Agents can find each other using the world’s BulletinBoard (or other mechanisms defined by the world simulation), they can act in the world, receive state from it and – most importantly for our current purposes – request/run negotiations involving other agents (through dedicated Controller and/or Negotiator objects).

A picture is worth a thousand words. The following figure shows how all the classes we mentioned so far fit together

The most important points to notice about this figure are the following:

  • Almost all entities are NamedObjects which means they have a user assigned name used for debugging, printing, and logging, and a system assigned id used when programatically accessing the object. For example, agents request negotiations with other agents from the world using the partner’s id not name.

  • Controller objects can access neither worlds nor mechanisms directly and they depend on agents to create them and on negotiators to negotiate for them.

  • A UtilityFunction in negmas is an active entity, it is not just a mathematical function but it can have state, access the mechanism state or settings (through its own AgentMechanismInterface) and can change its returned value for the same output during the negotiation. Ufuns need not be dyanmic in this sense but they can be.

Mechanisms (Negotiations)

The base Mechanism class is implemented in the mechanisms module.

All protocols in the package inherit from the Protocol class and provide the following basic functionalities:

  • checking capabilities of agents against requirements of the protocol

  • allowing agents to be join and leave the negotiation under the control of the underlying protocol. For example the protocol may allow or disallow agents from entering the negotiation once it started, it may allow or disallow modifying the issues being negotiated, may allow only a predefined maximum and minimum number of agents to engage in the negotiation. All of this is controlled through parameters to the protocol initializer.

  • provide the basic flow of protocols so that new protocols can be implemented by just overriding a single round() function.

  • provide basic callbacks that can be extended by new protocols.

    Protocols must extend any callback (i.e. call the super() version) instead of overriding them as they may do some actions to ensure correct processing.

The simplest way to use a protocol is to just run one of the already provided protocols. This is an example of a full negotiation session:

p = SAOMechanism(outcomes = 6, n_steps = 10)
p.add(LimitedOutcomesNegotiator(name='seller', acceptable_outcomes=[(2,), (3,), (5,)]))
p.add(LimitedOutcomesNegotiator(name='buyer', acceptable_outcomes=[(1,), (4,), (3,)]))
state = p.run()

You can create a new protocol by overriding a single function in the Protocol class.

The built-in SAOMechanism calls negotiators sequentially. Let’s implement a simplified similar protocol that asks all negotiators to respond to every offer in parallel.

from concurrent.futures import ThreadPoolExecutor
class ParallelResponseMechanism(Mechanism):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.current_offer = None
        self.current_offerer = -1

    def round(self):
        n_agents = len(self.negotiators)
        current = self.negotiators[(self.current_offerer + 1) % n_agents]
        self.current_offer = current.propose(self.state)

        def get_response(negotiator, offer=self.current_offer,
            return negotiator.respond(state, offer)

        with ThreadPoolExecutor(4) as executor:
            responses = executor.map(get_response, self.negotiators)
        self.current_offerer = (self.current_offerer + 1) % n_agents
        if all(_== ResponseType.ACCEPT_OFFER for _ in responses):
            return MechanismRoundResult(agreement=self.current_offer)
        if any(_== ResponseType.END_NEGOTIATION for _ in responses):
            return MechanismRoundResult(broken=True)
        return MechanismRoundResult()

We needed only to override the round method which defines one round of the negotiation. The protocol goes as follows:

  1. Ask the next negotiator to propose.

  2. Get the response of all negotiators (using the thread-pool)

  3. If all negotiators accept the current offer, return it as the agreement

  4. Otherwise, if any negotiators responded with END_NEGOTIATION, break the negotiation

  5. Otherwise, change the next negotiator and return.

Note that we did not need to take care of timeouts because they are handled by the base Mechanism class. Nor did we need to handle adding agents to the negotiation, removing them (for dynamic protocols), checking for errors, etc.

Agents can now engage in interactions with this protocol as easily as any built-in protocol:

p = ParallelResponseMechanism(outcomes = 6, n_steps = 10)
p.add(LimitedOutcomesNegotiator(name='seller', acceptable_outcomes=[(2,), (3,), (5,)]))
p.add(LimitedOutcomesNegotiator(name='buyer', acceptable_outcomes=[(1,), (4,), (3,)]))
state = p.run()

The negotiation ran with the expected results

Our mechanism keeps a history in the form of a list of MechanismState objects (on per round). Let’s check it:

import pandas as pd
pd.DataFrame([vars(_) for _ in p.history])
running waiting started step time relative_time broken timedout agreement results n_negotiators has_error error_details
0 True False True 0 0.001257 0.0 False False None None 2 False
1 True False True 1 0.002331 0.1 False False None None 2 False
2 False False True 2 0.003132 0.2 False False (3,) None 2 False

We can see that the negotiation did not time-out, and that the final agreement was (3,) but that is hardly useful. It will be much better if we can also see the offers exchanged and who offered them.

To do that we need to augment the mechanism state. NegMAS defines an easy way to do that by defining a new MechanismState type and filling it in the mechanism:

from dataclasses import dataclass

class MyState(MechanismState):
    current_offer: Outcome = None
    current_offerer: str = "none"

class NewParallelResponseMechanism(ParallelResponseMechanism):

    def __init__(self, *args, **kwargs):
        kwargs['state_factory'] = MyState
        super().__init__(*args, **kwargs)

    def extra_state(self):
        if self.current_offerer >= 0:
            current = self.negotiators[self.current_offerer].name
            current = "none"
        return dict(
            current_offer = self.current_offer,
            current_offerer = current

That is all. We just needed to define our new state type, set the state_factory of the mechanism to it and define how to fill it in the extra_state method. Now it is possible to use this mechanism as we did previously

p = NewParallelResponseMechanism(outcomes = 6, n_steps = 10)
p.add(LimitedOutcomesNegotiator(name='seller', acceptable_outcomes=[(2,), (3,), (5,)]))
p.add(LimitedOutcomesNegotiator(name='buyer', acceptable_outcomes=[(1,), (4,), (3,)]))
print(f"Agreement: {p.state.agreement}")
Agreement: (3,)

We can now check the history again (showing few of the attributes only) to confirm that the current offer and its source are stored.

def show_history(p):
    """Returns a Pandas Dataframe with the negotiation history"""
    return pd.DataFrame([
        for _ in p.history])
step agreement relative_time timedout broken current_offer current_offerer
0 0 (3,) 0.0 False False (3,) seller

Let’s see what happens if agreement is impossible (no intersection of acceptable outcomes in our case):

p = NewParallelResponseMechanism(outcomes = 6, n_steps = 6)
p.add(LimitedOutcomesNegotiator(name='seller', acceptable_outcomes=[(2,), (0,), (5,)]))
p.add(LimitedOutcomesNegotiator(name='buyer', acceptable_outcomes=[(1,), (4,), (3,)]))
print(f"Agreement: {p.state.agreement}")
Agreement: None
step agreement relative_time timedout broken current_offer current_offerer
0 0 None 0.000000 False False (5,) seller
1 1 None 0.166667 False False (3,) buyer
2 2 None 0.333333 False False (2,) seller
3 3 None 0.500000 False False (1,) buyer
4 4 None 0.666667 False False (0,) seller
5 5 None 0.833333 False False (1,) buyer
6 6 None 1.000000 True False (1,) buyer

As expected, the negotiation timed out. Let’s try to make it possible for the agents to agree by providing a common outcome that they may agree upon:

p = NewParallelResponseMechanism(outcomes = 6, n_steps = 6)
p.add(LimitedOutcomesNegotiator(name='seller', acceptable_outcomes=[(3,), (0,), (5,)]))
p.add(LimitedOutcomesNegotiator(name='buyer', acceptable_outcomes=[(1,), (4,), (3,)]))
print(f"Agreement: {p.state.agreement}")
Agreement: (3,)
step agreement relative_time timedout broken current_offer current_offerer
0 0 None 0.000000 False False (5,) seller
1 1 None 0.166667 False False (4,) buyer
2 2 None 0.333333 False False (5,) seller
3 3 (3,) 0.500000 False False (3,) buyer

We got an agreement again as expected.

Worlds (Simulations)

A world in NegMAS is what connects all agents together. It has a simulation_step that is used to run a simulation (or update the state from the real world) and manages creation and destruction of AgentWorldInterfaces (AWI) and connecting them to Agents.

Agents can join and leave worlds using the join and leave methods and can interact with it through their AWI.

To create a new world type, you need to override a single method (simulation_step) in the base World class to define your simulation. Most likely you will also need to define a base Agent inherited class that is capable of interacting with this world and a corresponding AgentWorldInterface.

You can see an example of a world simulation in the tutorials.