An Agent that Facilitates Crowd Discussion

Online discussion platforms are perceived as the next-generation method of citizen involvement. Such platforms can collect, integrate, and synthesize opinions to achieve social good. Crowd-scale platforms are being developed and deployed in social experiments that involve citizens and local governments. In such platforms, human facilitation is often used to preserve the quality of the discussions. Human facilitators often face difficulties when the discussions grow in size. In this paper, we present “D-agree, ” a crowd-scale discussion support system based on an automated facilitation agent. The agent extracts discussion structures from online discussions, analyzes them, and posts facilitation messages. We conducted small- and large-scale social experiments in Japan to assess the social impact of the platform. The results showcase the success of our automated facilitation agents in gathering valuable opinions from citizens. In addition, our experiments highlight the effect of an automated facilitation agent on online discussions. In particular, we find that combining the agent facilitator with human facilitators leads to higher user satisfaction

E-contact facilitated by conversational agents reduces interethnic prejudice and anxiety in Afghanistan

AIエージェントによって分断された民族間の偏見と不安を軽--対立グループのE-ContactにおけるAIエージェントの効果-- 京都大学プレスリリース. 2024-04-04.Don't matter if you're this or that: AI intervention mitigates tension among conflicting ethnic groups. 京都大学プレスリリース. 2024-05-13.Intergroup contact occurring through indirect means such as the internet has the potential to improve intergroup relationships and may be especially beneficial in high conflict situations. Here we conducted a three-timepoint online experiment to ascertain whether the use of a conversational agent in E-contact platforms could mitigate interethnic prejudices and hostility among Afghanistan’s historically segregated and persistently conflictual ethnic groups. 128 Afghans of Pashtun, Tajik, and Hazara backgrounds were assigned to one of four E-contact conditions (control with no conversational agent and three experimental groups that varied in the conversational agent settings). Participants in the experimental conditions contributed more ideas and longer opinions and showed a greater reduction in outgroup prejudice and anxiety than those in the control group. These findings demonstrate that E-contact facilitated by a conversational agent can improve intergroup attitudes even in contexts characterized by a long history of intergroup segregation and conflict

A baseline for non-linear bilateral negotiations: the full results of the agents competing in ANAC 2014

In the past few years, there is a growing interest in automated negotiation in which software agents facilitate negotiation on behalf of their users and try to reach joint agreements. The potential value of developing such mechanisms becomes enormous when negotiation domain is too complex for humans to find agreements (e.g. e-commerce) and when software components need to reach agreements to work together (e.g. web-service composition). Here, one of the major challenges is to design agents that are able to deal with incomplete information about their opponents in negotiation as well as to effectively negotiate on their users’ behalves. To facilitate the research in this field, an automated negotiating agent competition has been organized yearly. This paper introduces the research challenges in Automated Negotiating Agent Competition (ANAC) 2014 and explains the competition set up and results. Furthermore, a detailed analysis of the best performing five agents has been examined

Addressing Complexity in Multi-Issue Negotiation via Utility Hypergraphs

There has been a great deal of interest about negotiations having interdependent issues and nonlinear utility spaces as they arise in many realistic situations. In this case, reaching a consensus among agents becomes more difficult as the search space and the complexity of the problem grow. Nevertheless, none of the proposed approaches tries to quantitatively assess the complexity of the scenarios in hand, or to exploit the topology of the utility space necessary to concretely tackle the complexity and the scaling issues. We address these points by adopting a representation that allows a modular decomposition of the issues and constraints by mapping the utility space into an issue-constraint hypergraph. Exploring the utility space reduces then to a message passing mechanism along the hyperedges by means of utility propagation. Adopting such representation paradigm will allow us to rigorously show how complexity arises in nonlinear scenarios. To this end, we use the concept of information entropy in order to measure the complexity of the hypergraph. Being able to assess complexity allows us to improve the message passing algorithm by adopting a low-complexity propagation scheme. We evaluated our model using parametrized random hyper- graphs, showing that it can optimally handle complex utility spaces while outperforming previous sampling approaches