CIG: Cultural Islands and Games
John Dickerson, Vanina Martinez, Diego Reforgiato, Aaron Mannes
V.S. Subrahmanian
University of Maryland
vs@cs.umd.edu
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Motivation
• Many applications require the understanding of foreign cultures:
– Businessmen traveling overseas would like to have a quick “virtual experience”
on how to greet or approach a counterpart from a different culture.
– Tourists may like to “sample” different countries before deciding where to
travel.
– UN peacemakers that are going to deploy into a specific foreign region could
engage in a virtual training session to increase understanding of the different
groups that reside in the area.
• Information about geopolitical actors is widespread:
– Specific studies on groups behavior.
– Lots of data in the news, blogs, and social media can be used for modeling
behavior.
• Virtual world technology allows the creation of immersive environments
where people interact with other players, either humans or bots.
• CIG (Cultural Islands and Games) is an attempt to exploit these
motivations.
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What is a CIG?
• A computational massive, multiplayer, online game environment that
allows users to quickly focus on one part of the world or one group.
• A user will be able to:
– Understand background of the culture and socio-political practices,
– Learn how to interact with members of these groups, based on a rich
understanding of their behaviors, and automatically learned, statistically valid
behavioral models,
– Identify the history of activities of the group,
– Interact with computational models of these groups,
– Experiment with “what-if” scenarios,
– Forecast what the group might do in a given situation in order to be able to
determine which action should be taken to best achieve one objectives,
– Interact with other users reasoning about the same group.
•
We have built two CIG environments, CAVE and SAGE: the first within
Second Life, the second a combination of Second Life and Java.
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CIT (Cultural Island Toolkit)
• CIG is a part of the Cultural Island Toolkit (CIT) architecture.
• The CIT architecture when completed will:
– Allow multiple experts and analysts to securely congregate on a virtual island,
– Promote the formation of a community of experts on this island,
– Provide an online gaming environment that will allow experts to play out
“what if” scenarios involving current and future policy,
– Use statistically accurate behavioral models in determining actors’ actions,
– Allow a mix of human players and artificially intelligent bots to interact online,
– Provide analysis of gameplay that will serve as valuable input to the expert.
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Minorities
At Risk DB
DemoGraphic DB
Both
operational
Financial
Data
OASYS
Opinion
Analysis Sys.
News
Sources
T-REX RDF
Extractor
Blogs
Social
Media
Data Sources
Both
operational
SOMA
Terror
Org
Portal
SOMA
Behavioral
Model
Extraction
Engine
Cultural
Island
Execute
Tool
Behavioral
Model
Library
Real-time
Text
Analytics
Stochastic
Opponent
Modeling
Agents
LEGEND:
Green: Usable
Yellow: In progress
Red: Planned
Cultural
Island
Game
Tool
Cultural
Island
Toolkit
Distributed
User
Community
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CIT Architecture
• Takes data from real-time (news, blogs, social media) and legacy
(Minorities at Risk DB) sources.
• From this data, the SOMA Extraction Engine automatically finds rules of
the form:
“If condition C is true, then group G will execute action A with a probability of L to
U percent.”
• These stochastic rules serve as input to CIG and model the behavior of the
group (or groups) involved in the game.
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CIG Games
• Two kinds of players:
– Automated bots that play in accordance with SOMA behavioral models.
– Human players who play with and/or against each other and with/against the
bots.
• A player’s performance from the other players’ point of view can be
characterized by a cultural compatibility index (CCIN).
– The set C of CCIN vectors can be explicitly or implicitly specified.
• Q is a set of questions and answers posed to the user
• V is a set of animations, video clips, audio files, et cetera . . .
A sample CCIN vector
• The sets C, Q, and V form the basis for a CIG!
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CIG Games
• A cultural island game G based on (C,Q,V) is a tree, where:
– Node N in G is labeled with the triple (c, q, ANS(q)):
c ϵ C, q ϵ Q, ANS(q) is the set of all answers to q
– N has one child linked with each answer in ANS(q)
• Each node represents a game state that consists of:
– A cultural compatibility index vector
– A question posted to the user
– A deterministic set of possible answers.
• The next node (state) is determined depending on the selection made by
the game player.
First two levels of a simple game tree
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Cultural Island Authoring Tool
• This tool should be flexible enough to allow different kinds of game to be
built.
• Allows the user to:
–
–
–
–
–
Specify the content of a node,
Associate animations, audio, and text with a node,
Specify the question and set of possible answers associated with the node,
Specify how to determine the current user's performance rating,
Specify the children of the nodes.
• Intuitive and usable by non-technical users.
• Prototype built!
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Cultural Island Execute Tool
• Execute tool provides the execution environment for a game authored
within the CIG environment.
• Can aggregate single player games into multiplayer games.
• Also allows players to come and go from the game.
• Online accessible.
• Central database to feed and track the game at any point in time
– Contains game specifications and bots’ behavioral models.
• Connected to live SOMA system.
• For each step:
– The human user is posed with a question,
– The human user responds,
– Based on the user’s actions, the game queries the database and SOMA with
the current scenario to decide the responses from the other groups,
– The game displays the actions returned by SOMA,
– The game stores users’ (both human and bots) actions, as well as feedback
from the human user.
• Working on it!
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Multiagent Game
• A multiagent game is represented by a tree where:
– Each node maps multiple player IDs to triples (c, q, ANS(q)).
• Multiagent nodes are formed by merging nodes from single player CIG
games.
• This can be done automatically, allowing for players to enter and leave a
multiagent game with ease.
Game tree representing two users playing simultaneously
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Example CIG: CAVE
• CAVE: Cultural Afghan Village Experience.
• CAVE is a completed prototype implementation of a CIG.
• Focuses on acclimating US soldiers to Afghan culture by:
– Placing the soldier in a virtual Afghan village,
– Allowing ample opportunity for interaction with villagers (elders and workingclass) in a variety of situations,
– Providing visual feedback regarding the effectiveness of the player’s decisions.
• Feedback is returned via a CCIN vector consisting of the opinions of the
three main non-human players.
• Progression to higher levels determined by the human player’s adeptness
at making culturally acceptable decisions.
• DVD Installer Available!
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Example CIG: CAVE
• Opinion of non-human players based on the human player showing:
• An understanding of the local culture and customs,
• That he is a reliable partner with the interests of the village at heart.
• CAVE is not connected to SOMA. Instead, paths on the game tree are
based on the expertise of analysts and political experts.
• The CAVE game tree is large:
• Around 3,800 nodes!
• Deterministic.
• Built within Second Life game.
Screenshot of CAVE gameplay
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Demonstration
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Example CIG: SAGE
• SAGE: Strategic Afghan Gaming Environment.
• SAGE is a prototype implementation of CIG.
• SAGE is based solely on MAROB data, not online, single player version.
• Focuses on the relationships between two Afghan warring groups and the
US/Afghan governments:
– Hezb-i-Islami (Hekmatyar’s group),
– The Hazara tribe/community.
• Uses real models of behaviors of these groups – no longer uses handtailored data.
• Built jointly by computer scientists and policy experts.
• Incorporates aspects of Second Life.
• Prototype being built!
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Notion of state in SAGE
• State S consists of:
– A set of environmental variables: conditions of the world
• The user sets the values to create “what-if” scenarios.
• This replaces the set Q of questions mentioned earlier.
• ANS(q) is replaced by the set of all possible combinations of these
environmental variables.
– A set of facts: environmental conditions for which the user cannot
change the values,
– A set of animations and multimedia files that depict the current state
of affairs, as well as actions taken by users,
– No explicit notion of CCIN: performance is visualized from how the
groups react to the user’s decisions.
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SAGE Authoring Tool
• A single tool that allows the user to:
– Design the flow of the game
– Define aspects of the game
• Import an .xls file or database containing data:
– Probabilistic model of the opponent group
– “In a world defined by environmental variables E, with what action A will group
G respond? With what probability will G respond with this action?”
• GUI used to set up all other aspects of the game:
– Set the structure of the game (flow among states)
– Link animations, pictures, audio, and text
– Incorporate the imported database of game data
– Set possible actions/outcomes for the user
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Demonstration
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