My job:
Team collaboration is a key theme in MOBA (Multiplayer Online Battle Arena) games. Players collaborate through various kinds of conditions to accomplish different complex tasks to gain the final victory for their team. Recent years have seen the rise and prosperity of many team-based competitive online games, such as Riot Games’ League of Legends, Valve’s DOTA 2, Blizzard Entertainment’s Heroes of the Storm, etc. In these games, strangers form a temporary team to defeat another team. In order to achieve the victory, team members need to collaborate intensively and have efficient interaction with each other.
To study how players collaborate in the MOBA games, we focused on the Riot Games’ League of Legends as it is one of the most popular one in this genre. It is not hard for us to find players of different levels such as inexperienced and experienced players. When playing the game, temporary teams meet various challenges in communication and coordination, such as lack of organization and deviant behavior. To achieve success, players should not only discipline themselves but also exert positive influence on their teammates.
The main problem in this process is that whether players can have efficient collaboration with the existing game mechanism and auxiliary functions. We need to find out specific in-game obstacles which impede players from building up intensive collaboration with each other. There are also many complicated problems regarding to players themselves, such as insult, conflicts, different strategies and indifference.
Why a chatbot might be helpful?
Chatbot has been applied in many aspects of society with the development of information technology. For example, in many business companies, today’s chatbots are intelligent enough to engage potential users and ensure that human customer service and sales agents are not swamped with repetitive inquiries that waste time and resources. But that’s not all they do. Companies use chatbots in very interesting ways that demonstrate the full spectrum of the capabilities and capacities of chatbots.
As for the specific purpose of increasing collaboration in game scenario, the chatbot can be designed to help by serving as a virtual assistant, which simulates small talking of human. It does not play the role of an instructor in the game which may influence the team strategies and guide the team to win. Instead, it uses pre-designed words to provide additional useful information for the team members and engage them to better communicate with each other. In this way can the chatbot help to promote team collaboration.
How can a chatbot help collaboration among players of League of Legends in a constructive and user-friendly way?
We implemented the initial interviews to gather general impressions of team collaboration. We reached out to our friends who play League of Legends to do online or face-to-face interviews. As we didn’t have much knowledge scope of the players’ thoughts regarding to the HCI problem in League of Legends, we try to expand players’ information as much as possible and achieve a holistic view from a few users. Interview questions were formulated with four sections，including icebreaker questions, group filter questions, player collaboration questions and how they rate themselves as an LoL player.
After the initial interview, we had a general understand of players problems regarding to team collaborations in their game play. To further specify these problem through discussions, we conducted two rounds of focus group to learn more about different players attitudes towards same problems. Based on the design of the initial interviews, questions that were used in the focus groups follows the same structure as initial interview questions.
The survey questions were formulated by synthesizing various themes that arose out of the initial interview questions. We designed the questions in such a way that we could gather quantitative data on the qualitative comments offered by our initial interviewees. The items were a mixture of multiple choice, 6-point Likert scales, Yes/No dichotomous questions with added context, and an optional short answer for those who wished to provide additional feedback. The survey items were organized into five sections for ease of measurement and logical organization.
After reviewing the results of interviews, surveys and focus groups, we defined preliminary functions of our chatbot. There are five main categories of the functions.
Prototype Tool: AutoHotKey
It is a open source custom scripting language used to create keyboard shortcuts mapped to the chatbot’s functions. Typing commands in the chatbox would be too slow and imprecise, which may lead to participants discovering the authenticity of the chatbot prototype. However, AutoHotKey facilitates quick communication between the Wizard of Oz chatbot and the players. We organized the previously discussed commands according to the pre-defined categories that we had defined and attached shortcuts to them (e.g. Ctrl+W for “[Help] Your teammate needs help in the middle lane”).
The incorporation of keyboard shortcuts permitted a simulation of the speed, accuracy, and efficiency of a real-world chatbot system.
The whole session took about 40-50 mins and two test sessions were conducted, We asked players to treat the games as a 4v4 since it is difficult to insert chatbot function into this mature and complex game system and the time is limited at this stage, our chatbot can only function automatically at the cost of one player spot” to ensure the success of this Wizard of Oz study.
By combining the survey and interview data, we hoped to provide rich quantitative and qualitative information that could inform future recommendations and designs for the collaborative chatbot.
The gameplay experience measures included: difficulty coordinating with teammates in the chatroom where they pick champions, awareness of teammates expectations, difficulty in getting another player to help, self measure of teamwork, measurement of teammates’ teamwork, speed in response to calls for help, and frequency that others gave reasons for not coming to help.
The chatbot functionality measures included: desire to use the chatbot frequently, usefulness in coordination, enjoyment of the chatbot’s personality, interference of the chatbot in the normal flow of the game, appreciation for the chatbot directing teammates to help, level of seamlessness in integration of the system, how much the chatbot helped players feel like part of a team, whether the chatbot had any effect on how people played the game, and whether the chatbot helped players play better.
The interviewers went through each participants’ responses and asked them to give reasons for their ratings. This provided deep qualitative insight into the players’ experience with the chatbot and their suggestions for future design iterations.
As a result, we received positive feedbacks on the usefulness of our chatbot which suggest that our chatbot did improve players’ collaborations with their teammates without breaking the fairness in the game. Yet, there are still limitations to this project. More features can be added into the next generation of the chatbot and improvement can be made in the design of the future research.
Positive feedbacks:
Negative feedbacks:
