CIG: Cultural Islands and Games

1. CIG: Cultural Islands and Games John Dickerson, Vanina Martinez, Diego Reforgiato, Aaron Mannes V.S. Subrahmanian University of Maryland vs@cs.umd.edu

2. 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.

3. 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.

4. 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.

5. Minorities At Risk DB LEGEND:Green: Usable Yellow: In progress Red: Planned SOMA Terror Org Portal Both operational Both operational Demo- Graphic DB OASYSOpinion Analysis Sys. Financial Data SOMABehavioral Model Extraction Engine Cultural Island Execute Tool News Sources T-REX RDFExtractor Cultural Island Game Tool Blogs Behavioral Model Library Social Media Real-time Text Analytics Stochastic Opponent Modeling Agents Cultural Island Toolkit Distributed User Community Data Sources

6. 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.

7. 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!

8. 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

9. 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!

10. 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!

11. 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

12. 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 working-class) 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!

13. 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

14. Demonstration

15. 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 hand-tailored data. • Built jointly by computer scientists and policy experts. • Incorporates aspects of Second Life. • Prototype being built!

16. 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.

17. 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

18. Demonstration