TS3-2: Daiki Setoguchi from Nagoya Institute of Technology
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Please listen to the presentation, read detailed slides and return to first post to make your comments below the corresponding paper author's post. Session Chair: Ahmed Moustafa Session Theme: Machine Learning and Creativity Session Number: 3 Paper No: 14 Session and Talk No: TS3-2 Type: Short Co-authors: Daiki Setoguchi, Shun Okuhara, Ahmed Moustafa and Takayuki Ito Title: Alternating offers protocol by automated negotiation based on constraint relaxation
![Introduction
Automated Negotiation Agent
An agent that negotiates on behalf of human beings and aims
to obtain better negotiation results than human beings
Purpose of automated negotiation
• To make reasonable consensus on complex and large-scale
negotiations
• To simulate and analyze real-world negotiations
• To reduce the social burden (time, personnel) for negotiations
Example: sales negotiation, scheduling negotiation, transportation system, etc.
It is rarely actually applied in current research[1]
• There is a difference between the experimental setup used in the research
and the actual problem
• Users have difficulty trusting automated negotiation agent
2
[1] Baarslag, Tim, et al. "When will negotiation agents be able to represent us? The challenges and opportunities for autonomous negotiators." (2017): 4684-4690.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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TS3-2: Daiki Setoguchi from Nagoya Institute of Technology
- 1. Alternating offers protocol by automated negotiation based on constraint relaxation Nagoya Institute of Technology Kyoto University Daiki Setoguchi, Shun Okuhara, Ahmed Moustafa, Takayuki Ito
- 2. Introduction Automated Negotiation Agent An agent that negotiates on behalf of human beings and aims to obtain better negotiation results than human beings Purpose of automated negotiation • To make reasonable consensus on complex and large-scale negotiations • To simulate and analyze real-world negotiations • To reduce the social burden (time, personnel) for negotiations Example: sales negotiation, scheduling negotiation, transportation system, etc. It is rarely actually applied in current research[1] • There is a difference between the experimental setup used in the research and the actual problem • Users have difficulty trusting automated negotiation agent 2 [1] Baarslag, Tim, et al. "When will negotiation agents be able to represent us? The challenges and opportunities for autonomous negotiators." (2017): 4684-4690.
- 3. Shared and Local Issue Model(SL Model)[1] 3 [1] Okuhara, Shun, and Takayuki Ito. "A Compromising Strategy Based on Constraint Relaxation for Automated Negotiating Agents." Pacific Rim International Conference on Artificial Intelligence. Springer, Cham, 2019. There are shared issues and individual issues, and each agent advances negotiations by offering individual issues. During negotiations, each agent compromises by removing constraints The utility value of each proposed agreement is calculated based on the satisfied constraints.
- 4. Alternating Offers Protocol[2] 4 Agent A Agent B AcceptA AcceptB Bid Bid OfferA OfferB EndNegotiationA EndNegotiationB Action of the negotiator • Offer • Accept • EndNegotiation Bid : Candidates for a draft agreement [2] Rubinstein, Ariel. ”Perfect equilibrium in a bargaining model.” Econometrica: Journal of the Econometric Society (1982): 97-109.
- 5. Proposed Model SL Model • Negotiations for each constraint make it easy to explain the negotiation details • Uses a simultaneous proposal type negotiation model Alternating Offers Protocol • Alternate proposal type negotiation model • Since it is possible to refer to the proposal of the other party, it is easy to agree • Existing researches are difficult to explain because they negotiate by value rather than constraint 5 Proposal of alternating proposal type negotiation model that can explain negotiation result
- 6. Proposed Model 6 Agent A decision to end Agent B decision to end AcceptA AcceptB Offer with constraint relaxation Offer with constraint relaxation OfferA OfferB Yes Yes No No Start Agents negotiate by alternately proposing the process of constraint relaxation proposed in the SL model
- 7. Proposed Model 7 Accept No Start End Yes Constraint relaxation and Offer Accept Constraint relaxation and Offer Yes No Agent A Agent B
- 8. Experiment Number of simulation : 1000 Maximum round : 5 round Number of agent constraints : 5 Used Agent compromising method : random、min、distance、distance_min 8 Comparison of proposed model and SL model in terms of agreed number and consensus result
- 9. Used Agent 9 1 2 3 1 2 3 Agent Issue Ii1 C1 C2 1 2 3 1 2 3 Agent 1 2 3 1 2 3 Agent 1 2 3 Issue Is Random Min. Distance Distance_min. C1 C2 C1 C2 C3 1 2 3 1 2 3 Agent 1 2 3 C1 C2 C3 C4 Either C1 or C2 is deleted Delete C2 which is low utility Delete C3 which is far from Is Delete C4 which is far from Is and low utility Issue Ii1 Issue Is Issue Ii1 Issue Is Issue I1 Issue IsIssue Ii2 Issue Ii2 100 50 100 50 10 40 Negotiate with four agents with different methods
- 10. Result Compromising method Round Social Welfare Agreed num Agreed Social Welfare random 3.244 23.166 189 122.574 min 3.164 25.879 209 123.821 distance 3.18 25.657 205 125.158 distance_min 3.204 24.348 199 122.354 10 Compromising method Round Social Welfare Agreed num Agreed Social Welfare random 3.252 23.625 187 126.335 min 3.232 23.295 192 121.326 distance 3.244 23.123 189 122.344 distance_min 3.256 23.076 186 124.067 • Proposed Model • Conventional SL Model
- 11. Conclusions Background and Purpose • Automated negotiation agents that negotiate on behalf of human beings are attracting attention • It is necessary to create a model that is easy for humans to explain in order to apply it to actual negotiation problems Proposed Model • Alternating offers protocols by automated negotiation based on constraint relaxation • Negotiations for each constraint make it easy to explain the negotiation details • As a result of experiments, we found that the proposed model has more contracts than the existing model. Future works • Perform simulation by increasing agent 11
Editor's Notes
- I would like to talk about my research titled “Alternating offers protocols by automated negotiation based on constraint relaxation”. Now, let me begin.
- This is research introduction. The research theme is automated negotiation agent. To explain, an agent whose purpose is to negotiate on behalf of human beings and to obtain better negotiation results than human beings. In particular, it helps humans negotiate better by: Making reasonable consensus on complex and large-scale negotiation problems. Simulating and analyzing real negotiations. and reducing the social burden of negotiations And so on. However, it has been pointed out that current research is rarely applied to real problems. The reason is, こちらが研究背景のスライドとなります 研究背景といたしまして自動交渉エージェントというものがございます． これは人間の代理として交渉を行い，人間よりも良い交渉結果を得ることを目的としたエージェントです． このエージェントが行う自動交渉の目的として，複雑で大規模な交渉問題での合理的な合意形成を行うこと，現実の交渉をシミュレートして解析すること，交渉にかかる社会的負担を軽減することなどが挙げられます． しかし、現在の研究は実際の問題に応用されることはほとんどないことが指摘されています。 その理由は、
- I will explain the SL model handled in this research. In the SL model, there are shared issues and individual issues, and each agent advances negotiations by offering shared issues. The negotiating agents try to compromise by relaxing the constraints. Utility value means the score of how happy users are, the utility value of each candidate agreement is calculated according to the satisfied constraints. In this model, the constraints are easy for humans to explain, so the content of the negotiation can be clarified. 本研究で取り扱う，SLモデルについて説明をさせていただきます． SLモデルでは共有論点と個人論点というものがあり，各エージェントは共有論点をOfferし合うことで交渉を進めます． 効用値はユーザがどの程度嬉しいのかというスコアを意味するのですが， 各合意案候補の効用値は満たした制約によって計算されます． このモデルは制約が人間にとって説明しやすい為、交渉の内容を明確にすることができます。
- This figure is Alternating Offers Protocol proposed by Rubinstein. There is a lot of research on Alternative offers, and game theory and heuristic based approaches have been taken, and Alternative offers are defined from three actions, using bids that are contract candidates. The first action is an Offer that proposes a Bid that the agent wants to propose from among the candidate proposals, the second action is an Accept that accepts the Bid that the negotiating partner has proposed, and the third action is an EndNegotiation that is the action to abandon the negotiation . ’’’ I will explain the series of flows. First, Agent A performs Offer, and passes the selection right to Agent B. Agent B decides whether to accept the proposed Bid. If Agent B accepts it, the negotiation will be terminated with the proposal proposed by Agent A as the agreement proposal. If it does not accept, Agent B proposes Bid as a counter offer, and passes the selection right to Agent A. Agent A decides whether to accept as well. This process is repeated until an offer is accepted, or an agent sends End. ’’’ この図はルビンスタイン（Rubinstein） が提唱する Alternating Offers プロトコルです． Alternating Offers に関する研究は多く存在し，ゲーム 理論やヒューリスティックスに基づくアプローチがなされている Alternating Offersでは，合意案候補(Bid)を扱い，３つの行動から定義される． 一つ目はエージェントが合意案候補の中から自分の提案したいBidを提案するOffer，2つ目は交渉相手が提案してきたBidを受け入れるOffer，３つめは交渉を放棄するEndNegotiationの３つの行動です． 一連の流れについて説明します． まずエージェントAがOfferを行い，選択権をエージェントBに渡します．エージェントBは提案されたBidに対して，受容するかどうかを決定します． 受容する場合はエージェントAが提案したBidを合意案として交渉を終了します． 受容しない場合，エージェントBがカウンターオファーとしてBidを提案し，選択権をエージェントAに渡します． エージェントAは同様に受容するかどうかを決定します． 一連の流れを繰り返し行い，合意案を決定します． もし，これ以上交渉したく無い場合はEndNegotiationを選択することができ，その場合は交渉決裂として終了します．
- It is a feature of SL model. I propose the application of SL model using Alternating Offers Protocol It is a feature of Alternating Offers Protocol I propose an alternate proposal type negotiation model that can explain the negotiation result
- In my proposed model, agents negotiate by alternately proposing the constraint relaxation process proposed in the SL model. Negotiation agent can choose the same action as Alternating Offers Protocol, and proceed with the negotiation in the same way as Alternating Offers Protocol. The negotiation result is performed for each constraint, so it is easy to explain to humans. Also, this makes it easier to agree than the SL model, which is a simultaneous negotiation. 提案モデルでは、エージェントはSLモデルで提案されている制約緩和のプロセスを交互提案することで交渉を行う。 交渉エージェントはAlternating Offersと同様の行動を選択でき、Alternating Offersと同様に交渉を進めていく。 これによって交渉結果が制約ごとに行われているので人間への説明が容易である。 また、同時交渉であるSLモデルより合意しやすいモデルになる。
- I will explain the series of flows. First, Agent A offers Agent B. Agent B decides whether to accept the Offer. Next, if Agent B is not accepted, it relaxes the constraints referring to the proposal proposed by Agent A, and offers to Agent A. After that, Agent A does the same. Do this repeatedly and negotiate until one agrees. If no agreement is reached, the negotiation will be broken. 一連の流れについて説明します。 まず、エージェントAはエージェントBにOfferします。エージェントBはそのOfferを受け入れるかどうかを決定します。 エージェントBは受け入れられない場合はエージェントAの提案した案を参考に制約緩和を行い、エージェントAにOfferします。 その後、エージェントAも同様に行います。 これを繰り返し行い、どちらかが合意するまで交渉します。 合意されなかった場合は交渉決裂します。
- Here is the experimental setup. I compared the number of agreements and the agreement results between the proposed model and the SL model. The number of experiments is 1000 and the maximum round is 5. Each agent has 5 constraints. I also experimented with four methods of agent constraint relaxation: random, min, distance, and distance_min. こちらが実験設定です。 合意数と合意結果について提案モデルとSLモデルの比較を行いました。 実験回数は1000回であり、最大ラウンドは５ラウンド。 各エージェントの持つ制約は5つに設定しました。 また、エージェントの持つ制約緩和の手法はrandom, min, distance, distance_minの４つについて実験しました。
- This is the agent constraint relaxation method used in the experiment. Random is an agent that randomly removes constraints from the constraints of the agent. Min is an agent that removes the constraint with the lowest utility value from the constraints of the agent. Distance is an agent that removes the farthest constraint from Is among the constraints of the agent. Distance_min is an agent that removes a constraint with a low utility value far from Is in the constraints of the agent. これが実験で用いるエージェントの制約緩和の手法です。 Randomはエージェントの持つ制約の中で制約をランダムに取り除くエージェントです。 Minはエージェントの持つ制約の中で一番効用値の低い制約を取り除くエージェントです。 Distanceはエージェントの持つ制約の中でIsからもっとも遠い制約を取り除くエージェントです。 Distance_minはエージェントの持つ制約の中でIsから遠く効用値の低い制約を取り除くエージェントです。
- Here are the experimental results. Round average, social welfare average, number of agreements, social welfare when agreed. Regarding the number of rounds and the number of agreements, the proposed model performed well on all agents. Social welfare has good results for agents with min, distance and distance_min. The agreed social welfare has good results for agents with min and distance. これが実験結果です。 ラウンドの平均、社会的余剰平均、合意数、合意したときの社会的余剰の表です。 ラウンド、合意数は提案したモデルが全てのエージェントでよい結果を出しました。 社会的余剰はmin, distance, distance_minのエージェントについて良い結果を得られました。 合意した社会的余剰がmin, distanceのエージェントについて良い結果を得られました。
- It is a conclusions slide. Thank you for listening.