ABSTRACT Understanding how human beings delegate tasks to trustees when presented with reputation information is important for building trust models for human-agent collectives. However, there is a lack of suitable platforms for building large scale datasets on this topic. We describe a demonstration of a multi-agent game for training students in the practice of Agile software engineering. Through interacting with agent competitors in the game, the behavior data related to users&apos; decision-making process under uncertainty and resource constraints are collected in an unobtrusive fashion. These data may provide multi-agent trust researchers with new insight into the human decision-making process, and help them benchmark their proposed models

Chunyan Miao

Han Yu

Jun Lin

Su Fang Lim

Xinjia Yu

Zhiqi Shen

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A Multi-Agent Game for Studying Human Decision-making
(Demonstration)
Han Yu, Xinjia Yu, Su Fang Lim, Jun Lin, Zhiqi Shen, and Chunyan Miao
Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)
Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
{han.yu, xyu009, sflim2, jlin7, zqshen, ascymiao}@ntu.edu.sg
ABSTRACT
Understanding how human beings delegate tasks to trustees
when presented with reputation information is important for
building trust models for human-agent collectives. Howev-
er, there is a lack of suitable platforms for building large
scale datasets on this topic. We describe a demonstration of
a multi-agent game for training students in the practice of
Agile software engineering. Through interacting with agent
competitors in the game, the behavior data related to user-
s’ decision-making process under uncertainty and resource
constraints are collected in an unobtrusive fashion. These
data may provide multi-agent trust researchers with new
insight into the human decision-making process, and help
them benchmark their proposed models.
Categories and Subject Descriptors
I.2.11 [Artificial Intelligence]: Distributed Artificial In-
telligence - Intelligent Agents
Keywords
Reputation, trust, decision-making, bench-marking, game
1. INTRODUCTION
As agents are increasingly required to operate in socially
complex settings, understanding how people delegate tasks
to resource constrained trustees when presented with their
reputation information is of significant importance to multi-
agent systems (MASs) researchers [7, 8, 9]. However, there
is a lack of datasets to help MAS researchers gain insight
into this topic and benchmark proposed agent models.
In this paper, we describe a demonstration1 of a multi-
agent game based informatics platform - Agile Manager (AM)
- to help researchers understand how people delegate tasks
to trustee agents with different behavior patterns in an envi-
ronment characterized by uncertainty. The game is targeted
at people who wish to learn how to make efficient task alloca-
tion decisions in the Agile software development (ASD) prac-
tice [6]. It is capable of unobtrusively collecting the players’
in-game behavior data (including their mood, task allocation
1http://www.agelesslily.org/demo_agilemanager/
Appears in: Alessio Lomuscio, Paul Scerri, Ana Bazzan,
and Michael Huhns (eds.), Proceedings of the 13th Inter-
national Conference on Autonomous Agents and Multiagent
Systems (AAMAS 2014), May 5-9, 2014, Paris, France.
Copyright c© 2014, International Foundation for Autonomous Agents and
Multiagent Systems (www.ifaamas.org). All rights reserved.
strategy in response to the changes in trustee agents’ rep-
utation and emotional state, mouse movement trajectories,
etc.). It can potentially produce a large scale dataset with
new sources of information to provide insight into human
decision-making behaviors to MAS researchers, and build a
benchmark for future agent trust models.
2. DEMONSTRATION CONTENT
The AM game is a simulated environment where a player
can experience the challenges involved in managing an ASD
team. The game interface and system architecture are shown
in Figure 1. The player acts as the manager of an ASD team
of ten programmers each controlled by an agent. Each level
of the game consists of several Sprints of development. The
player needs to allocate a number of tasks to the programmer
agents (PAs) in each Sprint [6] to maximize the chance of
success of the software project. Each task is characterized
by its value, difficulty, required effort, and deadline.
Similar to real world ASD teams, each PA has differen-
t internal characteristics in terms of competence, capacity,
and emotion. Competence is the probability that a PA can
complete a given task with high quality. It is the ground
truth information used by the game system to generate PA
behaviors. It remains invisible to both the player and the
artificial intelligence (AI) competitors. Capacity represents
the maximum effort a PA can commit to working on assigned
tasks during a given Sprint. It is presented to the player in
the form of a workload bar as shown in Area C of Figure 1.
In odd numbered levels of the game, more competent PAs
also have larger capacities; whereas in even numbered levels,
more competent PAs have smaller capacities. The emotion
of a PA is calculated following the approach in [3]. It stems
from the OCC cognitive theory of emotions. The resulting
emotions are presented to the player in the form of the facial
expression of a PA exemplified by Area A of Figure 1.
Each PA has characteristics which are affected by the in-
teraction with the player. They include reputation and cur-
rent workload. A PA’s reputation depends on its past perfor-
mance in completing the given tasks with high quality and
on time. The higher the ratio between a PA’s competence
and the difficulty of the task assigned to it, the more likely
it is for the task to be completed with high quality. A PA’s
reputation is computed following the BRSEXT approach in
[8]. It is presented to the player in the form of a star rating
as shown in Area B of Figure 1.
A team of ten agents, with the same characteristics as the
PAs controlled by the player, allocate the same set of tasks a-
mong themselves following the SWORD approach [10]. This
1661
The Agile Manager game client
A
B
C
Player
AI
Game
Server
- Decision 
Data
- Decision 
Data
- Behavior 
Data
- Emotion 
Data
- Reputation
- Emotion
- Workload
- Capacity
PA 
Information
- Value
- Difficulty
- Effort
- Deadline
Task 
Information
Figure 1: An overview of the Agile Manager game platform.
team is the AI competitor against the player. The player
can benefit from the game by observing the AI’s strategy to
improve his/her decision-making.
3. DISCUSSIONS AND FUTURE WORK
As we aim to build a dataset on how people delegate tasks
to resource constrained trustees when presented with their
reputation information to support future research in multi-
agent trust modeling, the AM game collects a wide range of
data generated by the players. The types of data include:
1. every task allocation decision from the human and the
AI players (i.e., time taken between successive deci-
sions, which task was assigned to which PA, and s-
napshots of each PA’s current situation including its
perceived reputation and workload);
2. players’ self reported strategies used to allocate tasks
in each game session;
3. players’ self reported emotions after each game session
(i.e., a mix of 6 basic emotions [2]) and the correspond-
ing facial expressions (captured with AffectButton [1]);
4. mouse movement and mouse click positions in the game
interface during each game session;
5. effectiveness of persuasive computing techniques based
on the Elaboration Likelihood Model (ELM) [5];
6. players’ self reported profile information.
Beyond the work described here, the game is on track of
being used in large scale user studies. The resulting dataset
can potentially fill an important gap in existing reputation
ratings datasets, such as Epinions [4], which focus on re-
flecting people’s opinions on products instead of task allo-
cation decisions. In subsequent research, we will incorpo-
rate features enabling the game to study how to optimize
the usage of persuasive techniques for people from different
backgrounds to influence their decisions.
4. ACKNOWLEDGMENTS
This research is supported in part by Interactive and Dig-
ital Media Programme Office, National Research Founda-
tion hosted at Media Development Authority of Singapore
(Grant No.: MDA/IDM/2012/8/8-2 VOL 01).
5. REFERENCES
[1] J. Broekens and W.-P. Brinkman. AffectButton:
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[2] P. Ekman. An argument for basic emotions. Cognition
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[3] J. Lin, Ailiya, C. Miao, and Z. Shen. A FCM based
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[4] P. Massa and P. Avesani. Trust-aware bootstrapping
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[5] R. E. Petty and J. T. Cacioppo. Communication and
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[6] F. Shull, T. Dyba, and T. Dingsoyr. What do we know
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[8] H. Yu, Z. Shen, C. Leung, C. Miao, and V. R. Lesser.
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[9] H. Yu, Z. Shen, C. Miao, and B. An. Challenges and
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A multi-agent game for studying human decision-making.

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A multi-agent game for studying human decision-making.

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