[agents] CFP: The Automated Negotiating Agents Competition 2020 - Agent Negotiation and Elicitation

[Tim Baarslag]

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The Automated Negotiating Agents Competition 2020: Agent Negotiation and 
Elicitation

Challenge: Representing users in a negotiation - developing an agent 
that can negotiate while performing preference elicitation

Submission deadline: 1 May 2020

Event: IJCAI 2020, July 11-17 2020, Yokohama, Japan

Website: http://web.tuat.ac.jp/~katfuji/ANAC2020/genius.html
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====== Challenge ======
The challenge for 2020 is to design a negotiating agent that can elicit 
preference information from a user during the negotiation. The idea is 
that when a negotiating agent represents a user in a negotiation, it 
does not know exactly what the user wants, and therefore the agent needs 
to actively improve its user model through a preference elicitation process.

In previous years of ANAC, and in most literature on automated 
negotiation, the utility function of the agent is presumed known. As was 
introduced for the first time in 2019, the preferences of the agent will 
instead be given in the form of a ranking of a limited number of 
possible agreements. Additionally, this year, the agent may elicit 
further information about the ranking against an elicitation cost in 
order to improve the user model.


====== Event ======
The competition takes place during IJCAI 2020, July 11-17 2020, in 
Yokohama, Japan (https://www.ijcai20.org/). There is at least $5000 in 
total available for prize money and for student travel grants which will 
be made available to participants.


====== Entrants ======
Entrants to the competition have to develop and submit an autonomous 
negotiating party that runs on GeniusWeb 
(https://ii.tudelft.nl/GeniusWeb/). GeniusWeb is a negotiation platform 
in which you can develop general negotiating parties as well as create 
negotiation domains and preference profiles. The platform allows you to 
simulate negotiation sessions and run tournaments. Example parties and 
tutorials are available. If you are familiar with the Genius framework, 
it is easy to develop your agent in this new platform.

Performance of the parties will be evaluated in a tournament between all 
participants, where each party is matched with other submitted parties. 
All submitted parties will negotiate in a number of negotiation 
scenarios, with varying levels of preference uncertainty and elicitation 
costs.

We recommend developing a Java based party but you can also use Python 2 
using the python adapter available with GeniusWeb.


====== Negotiation ======
Rather than receiving a utility function, the party will receive an 
initial partial ordered profile 
(https://tracinsy.ewi.tudelft.nl/pubtrac/GeniusWeb#PartialOrdered). This 
profile will be an ordering of d bids, picked from an existing linear 
additive utility function U. To help the party, the partial profile will 
contain the best and worst possible bid.

The party negotiates with the partial profile until a certain outcome w* 
is reached. The score the party will receive for this outcome will be 
based on the original utility U(w*). So in short, the party receives 
ordinal information only, but it will be evaluated based on the 
underlying cardinal ground-truth.

The challenge for the parties is to design preference elicitation 
techniques in order to reach the best negotiation outcome, using 
techniques from e.g. machine learning, regression techniques, 
trade-offs, and linear programming. For details, please refer to the 
Genius manual and the frequently asked questions: 
https://docs.google.com/document/d/1vQhJFNxj5An5c6DenC9jfb7R7eebScme9NNWCVPhvfY/edit


====== Elicitation ======
The negotiations will be run using the SHAOP protocol 
(https://tracinsy.ewi.tudelft.nl/pubtrac/GeniusWeb/wiki/WikiStart#Protocol). 
In this protocol, the party can elicit more information about the true 
utility space in order to improve its own partial profile. This is done 
by requesting/eliciting a comparison from the "User". However, every 
such request comes at an elicitation cost, and all calls add up to a 
total bother. At the end of the negotiation, the performance of the 
party is recorded as the User utility of the final agreement, lowered 
with the total bother.

An elicitation action in the SHAOP protocol is implemented through the 
ElicitComparison and Compare actions. The ElicitComparison requests a 
bid to be compared to a set of alternative bids. The Comparison action 
tells which bids are better and worse than the given bid. The SHAOP and 
COB party can take these actions at any time, and need not follow the 
turntaking of the SHAOP protocol.

The SHAOP party knows the cost of each CompareBids request: it is 
contained in a parameter "elicitationcost" (or if not set, the default 
is 0.1). Each time CompareBids is called by a party, this cost is added 
to the total bother.
The SHAOP party will have to incorporate the Comparison result into the 
partial profile. One way to do this is to keep an ordered list, check 
the simpleshaop example party for details and example code.

Note: As of right now, the total bother is only implemented as constant 
increments of the elicitation cost. In theory however, it could be a 
very different function. All such possibilities are what we call bother 
cost functions. Future developments will explore extensions to support 
different types of bother cost functions.


====== Rules of Encounter ======
Negotiations are bilateral encounters following the SHAOP protocol. 
Offers are exchanged using a round-based protocol, with a maximum of 200 
rounds.

Parties do not have any prior knowledge about the preferences and 
strategy of the opponent. Parties are reset after each encounter; that 
is, agents may negotiate repeatedly on the same domain or with the same 
opponents, but they cannot learn from their previous interactions. When 
no agreement is struck, both parties receive the utility of their 
reservation bid, which is contained in the received profile. The 
reservation value can be different for both players. Note that this 
means a break-off can be preferable to an agreement for one (or both) of 
the players. This implies that it is risky to wait until the deadline to 
reach an agreement.

SHAOP specifies that the parties take turns making offers. One party 
starts the negotiation with an opening bid, after which the other party 
can take the following actions:

1.    Make a counter offer (thus rejecting and overriding the previous 
offer);
2.    Accept the offer;
3.    Walk away (i.e. ending the negotiation without any agreement and 
receiving the reservation value).

This process ends when either a party walks away, a party does an 
incorrect action, an agreement is reached or the deadline is reached 
(e.g. 200 rounds).

In order to make running the tournament feasible, parties are expected 
to generate their offers in a reasonable amount of time. Currently the 
GeniusWeb runserver imposes a response timeout of 10 seconds during 
negotiations.


====== Fair play ======
Agents will be disqualified for violating the spirit of fair play (e.g. 
hacking the API, starting threads, attempts to access other party’s 
preference profile). The board of the ANAC 2020 competition will be the 
judge on these matters (for more information, see 
http://ii.tudelft.nl/anac/). The competition rules allow multiple 
entries from a single institution, but require each agent to be 
developed independently.


====== Evaluation ======
The winners will be determined by the average individual utilities 
gained by each agent. There will be a separate prize for the most 
innovative elicitation strategy. The teams of the top agents will be 
notified, and the final results and awards will be announced at IJCAI 
2020. It is expected that teams that make it through to the finals will 
have a representative attending the conference.


====== Submission (Deadline: 1 May, 2020) ======
Participants submit their agent source code and class files (in a .zip 
or .jar package).

Submission package: Please submit your application though the following 
link: https://tinyurl.com/GENIUSANAC2020


====== Academic report ======
Each participant has the option to prepare a 2-4 page report describing 
the design of their agent according to academic standards. The best 
teams that submit a report will be given the opportunity to give a brief 
presentation describing their agent at IJCAI. Furthermore, proceedings 
of the competition are planned to be published in a special issue.

The report will be evaluated by the organizers of this league. For 
eligibility, the strategy design should provide a contribution to the 
negotiation community. The report is recommended to address the 
following aspects:

- Bidding Strategy: how the agent generates bid at its each turn;
- Acceptance Strategy: how the agent decides to accept or reject a given 
bid;
- Opponent Modelling: how the agent models the opponent (e.g. the 
opponent’s strategy, preferences etc.);
- Elicitation Method: how the agent deals with preference uncertainty, 
how the agent performs preference elicitation, and explaining which 
heuristics and/or machine learning method it employs for this purpose;
- Evaluation: an evaluation of the agent (either against itself or in a 
small-scale tournament setting).


====== Important Dates ======
Submission deadline: May 1, 2020
Notification to finalists: June 1, 2020 (tentative)
Event: July 11-17, 2020


====== Questions and Answers ======
Website: http://web.tuat.ac.jp/~katfuji/ANAC2020/genius.html
Getting started: https://ii.tudelft.nl/GeniusWeb/students.html
Feel free to consult the FAQ: 
https://docs.google.com/document/d/1vQhJFNxj5An5c6DenC9jfb7R7eebScme9NNWCVPhvfY/edit


Send your questions to:
- T.Baarslag at cwi.nl  (main contact for questions about the challenge)
- W.Pasman at tudelft.nl (for questions about implementation and GeniusWeb)
- reyhan.aydogan at ozyegin.edu.tr
- katfuji at cc.tuat.ac.jp