Supplementary Materials for "Generalized Bargaining Mechanisms" (Submitted to AAAI 2023 Senior Member Presentations Track)

This folder contains supplementary materials as well as all the code implementing the algorithms described in the paper and all datasets used.

Reproducibility and Public Availability

This guides shows how to reproduce every result in the paper and more.
The data is already publicly available in the GENIUS platform.
All the content of our supplementary materials will be made publicly available on Github in case of acceptance. Moreover, we will contribute our algorithms back the negotiation platform we use (NegMAS) as a pull request and if accepted will be available directly from the platform (which is community driven) in the future.

What is included?

supplementary.pdf Contains proofs and further experimental results and discussions.
code Implementation of algorithms, negative cases, and experiments.
- negmas A fork of the NegMAS platform including an implementation of a simplified version of GBMs GBM(1,1,other,.,.,.) under negmas/negmas/gb using which AOP, SAOP and TAU are implemented.
data All the negotiation scenarios used in the experiments.

Code

algorithms Contains implementations of the TAU protocol and SCS protocol. Implementations are modular and use a generalized version of the protocol description method introduced in the paper (Subsections Negotiation Protocol and Negotiation Strategy).
experiments All code needed to run the experiments reported in the paper and generate the figures and tables in it.
- experiment1.sh Runs experiment 1 and generates e1.csv with all raw results of the experiment.
- experiment2.sh Runs experiment 2 and generates e2.csv with all raw results of the experiment.
- analyze1.sh Runs analysis of the first experiment and generates Table 1 of the paper in latex format. It uses e1.csv generated by experiment1.sh. To just run the analysis on the data already in the data folder, change e1.csv in this script to e1.csv.
- analyze2.sh Runs analysis of the second experiment and generates Fig. 1 of the paper in latex format. It uses e2.csv generated by experiment2.sh. To just run the analysis on the data already in the data folder, change e2.csv in this script to e2.csv.
- experiment.py The actual code used to run all experiments (Called by the experiment shell scripts above). It is a command line utility that can be called with different arguments to run different experiments.
- eval1.py Used to analyze experiment 1 (flexible and can be adjusted to do other evaluations as well not reported in the paper).
- eval2.py Used to analyze experiment 2 (flexible and can be adjusted to do other evaluations as well not reported in the paper).
- utils.py Utility methods used by other python scripts
- helpers Helper scripts containing clean.py which is used in the second experiment to clean out extra runs if any and runs without controlled reserved value. In principle, it should not be needed.
- tests Contains scripts for testing AOP and TAU strategies on specific conditions.
  - testaop.py Tests an AOP strategy (default is MiCRO). You can run three simple experiments with it:
    - e1 (incomplete) shows that MiCRO and Atlas3 are not exactly complete using the tiny NiceOrDie scenario (other strategies may vary).
    - e2 (all-rational) shows success on the same domain when all outcomes are rational.
    - e3 (none-rational) shows expected disagreement on the same domain when no outcomes are rational.
    - e4 (small) runs the negotiation a relatively small domain (Outfit)
    - e5 (large) runs the negotiation on a relatively large domain (Kitchen)
    - e6 (difficult) runs the negotiation on a relatively large domain (Outfit) with larger reserved values (more difficult agreement)
    - free can be used to run on any domain.
  - testtau.py Tests the proposed SCS strategy on few example cases.
    - e1 (complete) shows that TAU($\infty, \infty$) is not exactly complete using the tiny NiceOrDie scenario (TAU($\infty, 0$) does get an agreement).
    - e2 (all-rational) shows success on the same domain when all outcomes are rational.
    - e3 (none-rational) shows expected disagreement on the same domain when no outcomes are rational.
    - e4 (small) runs the negotiation a relatively small domain (Outfit)
    - e5 (large) runs the negotiation on a relatively large domain (Kitchen)
    - e6 (difficult) runs the negotiation on a relatively large domain (Outfit) with larger reserved values (more difficult agreement)
    - free can be used to run on any domain.

data

domains All the negotiation domains used for the experiments reported in the papers. These are all the domains of the ANAC 2013 competition and they can also be found on the GENIUS platform. Each domain folder consists of the following files:
- {domain-name}.xml describes the outcome space in GENIUS XML format.
- {domain-name}1.xml describes the utility function of first negotiator (GENIUS XML format).
- {domain-name}2.xml describes the utility function of second negotiator (GENIUS XML format).
- stats.json Statistics of the negotiation scenario like opposition, welfare of the Nash Bargaining Solution, etc
- info.json More information about the negotiation scenario for different choices of reserved values including the fraction of rational outcomes f_rational.
Note that folder names prefix each domain with the size of its outcome space.
results Raw results of all negotiations in experiments (e1 for experiment 1 and e2 for experiment 2) as reported in the paper.
- stats Contains the results of all statistical tests reported in the paper (as well as others not reported due to lack of space).

Software Requirements

Python 3.9 or later (tested on MacOS with Python 3.10.5).
Java 18 (tested on MacOS with OpenJDK 18 2022-03-22 build 18+36-2087).
- only needed to run state-of-the-art negotiators (and Nice Tit for Tat)
negmas 0.9.2 (our forked version included in this code repository and installed when following the instructions in the following section).
genius-bridge v0.2.4 (installed when following the instructions in the following section).
- only needed to run state-of-the-art negotiators (and Nice Tit for Tat)

All experiments were done on MacOS 12.4 on a MacBook Pro with Apple M1 Pro processor and 16GB of RAM but should be reproducible on other operating systems as we only use OS-independent libraries in this work.

Installing Requirements

Please use your platform's preferred method to install Python 3.9+ and Java 18+.

To install other requirements run the following command within the code folder:

pip install -r requirement.txt
negmas genius-setup

Note that the later command will download the negmas-genius bridge and install in as:

$HOME/negmas/files/geniusbridge.jar

We assume that this is run within a virtual environment (as always recommended).

Running Experiments

Assumes that you installed requirements

To run the first experiment and generate Table 1, run the following within code/experiments:

> java -jar $HOME/negmas/files/geniusbridge.jar --silent --no-logs&
> experiment1.sh
> eval1.sh

To run the second experiment and generate Fig. 1, run the following within code/experiments:

> java -jar $HOME/negmas/files/geniusbridge.jar --silent --no-logs&
> experiment2.sh
> eval2.sh

These experiments may take a LONG time mostly because of the relatively slow speed of some state-of-the-art algorithms.
In case, the previous method did not work, it may be because of an issue with the NegMAS-GENIUS bridge. To investigate the issue you will need two terminals. In the first terminal run:
```
> java -jar $HOME/negmas/files/geniusbridge.jar --verbose --debug --no-logs
```
Now, In the second terminal run the remaining two lines. For example for the second experiment:
```
> experiment2.sh
> eval2.sh
```

Running example negotiations

You can use code/experiments/tests/test*.py to run single negotiations and test properties of TAU and AOP on different situations.

To run one of the five predefined examples given in the description above just pass its name. For example, to run the negotiation that shows that MiCRO is not complete, run:

> python testaop.py e1

> python testaop.py incomplete

To change the strategy pass it using --strategy, for example:

> python testaop.py e1 --strategy=Atlas3

To change the domain and run a free negotiation use the free option

> python testaop.py free --strategy=micro --domain=<path to the domain folder>

You can run similar negotiations using the TAU protocol

> python testtau.py e1

By default TAU will be used, but you can change $\beta$ by passing --beta. For example to show that TAUNR is not complete you can run:

> python testtau.py e1 --beta=-1

Passing --beta less than zero will be interpreted as $\infty$.

yasserfarouk/gbmaaai2023