Hello everyone! welcome to the presentation on Understanding Player Patterns by Combining
Knowledge-Based Data Abstraction with Interactive Visualization. This study was part of a DARPA
project and was performed at Northeastern University with the help of my co-authors
Casper and Magy. First and foremost, we would like to thank CHI play organizers for enabling
us to present our work despite the pandemic.
Although the field of player modeling is advancing rapidly, game designers are unable to reap
its benefits because of increasingly large datasets.
Though recent research has attempted to address this problem using techniques from data mining,
machine learning and data visualization, they often come with issues of interpretability
and transparency. Increasing amount of work is being conducted on obtaining human-readable,
interpretable rules for player performance prediction and winning strategies but they
still do not provide granular details on how the players actually play the game or interact
with different elements of the game.
In this work, we try to address the issues of transparency and interpretability in visualization
of high dimensional data with knowledge-based data abstraction, taking inspiration from
the field of medicine. We define Knowledge-Based Abstraction as the process of consolidation
of game state-space with the help of an expert to make the visualization human interpretable.
In this work, we presented an abstraction method which uses Glyph visualization system
proposed by Nguyen et al.Then we demonstrated the application of our method in the evaluating
a design of a puzzle game. Based on the lessons learned, we presented a general set of guidelines
for knowledge-based data abstraction to abstract player traces from raw data.
The game used in this study is called Daedalus. It is a slack-based digital escape room, which
revolves round a central narrative of a sentient AI testing players through a series of puzzles.
The game is a competition among teams with each team consisting of three to five players.
Teams attempt to complete all puzzles within a week. Players of a team can message one
another via slack to help each other figure out the solutions
We developed Daedalus to study adaptability in teams by investigating the problem-solving
behaviors in players. While player interviews and observations are possible to ensure the
game is achieving its purpose, they come with issues of subjective biases and poor scalability.
So we turned to objective analysis through visualization of player traces, which comes
with issues of interpretability and transparency. To get around these issues, we developed this
method of knowledge-based abstraction of player traces along with visualization.
The objectives of the analysis is to recognize different player patterns emerging and identify
puzzles that are most engaging, most frustrating and relatively easy to solve.
In the first step, expert prepared a list of states required to achieve the objectives
of analysis. This initial representation is then encoded with the low-level game logs
into states through data processing. Once processed, the abstracted game logs are fed
to Glyph to visualize the nodes and links. The visualization is then analyzed by the
experts to ensure that the state representation is one that is interpretable and can produce
meaningful results. Based on this analysis, the experts will further adjust the state
representation. The process of adjusting the state representation is done iteratively until
a human interpretable version is achieved.
During state representation adjustment, ideal trace and distance measure are also adjusted.
Ideal trace is like a frame of reference to compare patterns against and to see how participants
converge or diverge from the ideal behavior. Distance measure is adjusted to distinguish
the different sequences based on their length and content of the states in a sequence.
We refer to our paper for a detailed description of development of these elements.
In the final abstracted data visualization, We observed six different types of player
patterns based on the paths followed by the players. Here, the state graph to the left
indicates the different individual routes followed by the players. The sequence graph
on the right showcases an aggregate view of different types of emergent player patterns.
By qualitatively inspecting the sequences followed by players from different clusters,
we classified the players into six types. An example of dodger type player is shown
in the right. These type of players mostly rely on teammates after initial exploration
and failures. Examples sequences of other types of player patterns can be found in the
paper.
By applying our method, we have been able to identify both expected and unexpected player
patterns and some balancing issues.
We presented a set of general guidelines for knowledge-based abstraction of player traces
from games of other genres. However, we also acknowledge the limitations of our work in
terms of its suitability to RTS and MOBA games.
With that, we thank everyone for joining our presentation.