Behavior authoring for computer games involves writing behaviors in a programming language. This method is cumbersome and requires a lot of programming effort to author the behavior sets. Further, this approach restricts the behavior set authoring to people who are experts in programming.
This paper describes our approach to design a system that allows a user to demonstrate behaviors to the system, which the system uses to learn behavior sets for a game domain. With learning from demonstration, we aim at removing the requirement that the user has to be an expert in programming, and only require him to be an expert in the game. The approach has been integrated in a easy-to-use visual interface and instantiated for two domains, a real-time strategy game and an interactive drama.
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Authoring Behaviors for Games using Learning from Demonstration
by Manish Mehta, Santiago Ontañón, Tom Amundsen, Ashwin Ram
ICCBR-09 Workshop on Case-Based Reasoning for Computer Games, Seattle, July 2009
One of the main bottlenecks in deploying case-based planning systems is authoring the case-base of plans. In this paper we present a collection of algorithms that can be used to automatically learn plans from human demonstrations. Our algorithms are based on the basic idea of a plan dependency graph, which is a graph that captures the dependencies among actions in a plan. Such algorithms are implemented in a system called Darmok 2 (D2), a case-based planning system capable of general game playing with a focus on real-time strategy (RTS) games. We evaluate D2 with a collection of three different games with promising results.
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Learning from Human Demonstrations for Real-Time Case-Based Planning
by Santi Ontañón, Kane Bonnette, Praful Mahindrakar, Marco Gómez-Martin, Katie Long, Jai Radhakrishnan, Rushabh Shah, Ashwin Ram
IJCAI-09 Workshop on Learning Structural Knowledge from Observations, Pasadena, CA, July 2009
Currently many game artificial intelligences attempt to determine their next moves by using a simulator to predict the effect of actions in the world. However, writing such a simulator is time-consuming, and the simulator must be changed substantially whenever a detail in the game design is modified. As such, this research project set out to determine if a version of the first order inductive learning algorithm could be used to learn rules that could then be used in place of a simulator.
We used an existing game artificial intelligence system called Darmok 2. By eliminating the need to write a simulator for each game by hand, the entire Darmok 2 project could more easily adapt to additional real-time strategy games. Over time, Darmok 2 would also be able to provide better competition for human players by training the artificial intelligences to play against the style of a specific player. Most importantly, Darmok 2 might also be able to create a general solution for creating game artificial intelligences, which could save game development companies a substantial amount of money, time, and effort.
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Using First Order Inductive Learning as an Alternative to a Simulator in a Game Artificial Intelligence
by Katie Long
Undergraduate Thesis, College of Computing, Georgia Institute of Technology, Atlanta, GA, 2009
The design of interactive experiences is increasingly important in our society. Examples include interactive media, computer games, and interactive portals. There is increasing interest in modes of interaction with virtual characters, as they represent a natural way for humans to interact. Creating such characters is a complex task, requiring both creative skills (to design personalities, emotions, gestures, behaviors) and programming skills (to code these in a scripting or programming language). There is little understanding of how the behavior authoring process can be simplified with easy-to-use authoring environments that can support the cognitive needs of everyday users and help them at every step to easily carry out this creative task.
Our research focuses on behavior authoring environments that not only make it easy for novices/everyday users to create characters but also provide them scaffolding in designing these interactive experiences. In this paper we present results from a user study with a paper prototype of an authoring environment that is aimed to allow everyday users to create virtual characters. The study aims at determining whether typical computer users are able to create character personalities in specific scenarios and think about characters’ mental states, and if so, then what kinds of user interfaces would be suitable for this authoring environment.
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Creating Behavior Authoring Environments for Everyday Users
by Manish Mehta, Christina Lacey, Iulian Radu, Abhishek Jain, Ashwin Ram
International Conference on Computer Games, Multimedia, and Allied Technologies (CGAT-09), Singapore, May 2009
Some domains, such as real-time strategy (RTS) games, pose several challenges to traditional planning and machine learning techniques. In this paper, we present a novel on-line case-based planning architecture that addresses some of these problems. Our architecture addresses issues of plan acquisition, on-line plan execution, interleaved planning and execution and on-line plan adaptation. We also introduce the Darmok system, which implements this architecture in order to play Wargus (an open source clone of the well-known RTS game Warcraft II). We present empirical evaluation of the performance of Darmok and show that it successfully learns to play the Wargus game.
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On-Line Case-Based Planning
by Santi Ontañón, Neha Sugandh, Kinshuk Mishra, Ashwin Ram
Computational Intelligence, 26(1):84-119, 2010.
A growing research community is working towards employing drama management components in interactive story-based games. These components gently guide the story towards a narrative arc that improves the player’s experience. In this paper we present our Drama Management architecture for real-time interactive story games that has been connected to a real graphical interactive story based on the Anchorhead game. We also report on the natural language understanding system that has been incorporated in the system and report on a user study with an implementation of our DM architecture.
Developing a Drama Management Architecture for Interactive Fiction Games
by Santi Ontañón, Abhishek Jain, Manish Mehta, Ashwin Ram
1st Joint International Conference on Interactive Digital Storytelling (ICIDS-08), Erfurt, Germany, November 2008
We present a domain independent off-line adaptation technique called Stochastic Plan Optimization for finding and improving plans in real-time strategy games. Our method is based on ideas from genetic algorithms, but we utilize a different representation for our plans and an alternate initialization procedure for our search process. The key to our technique is the use of expert plans to initialize our search in the most relevant parts of plan space. Our experiments validate this approach using our existing case based reasoning system Darmok in the real-time strategy game Wargus, a clone of Warcraft II.
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Stochastic Plan Optimization in Real-Time Strategy Games
by Andrew Trusty, Santi Ontañón, Ashwin Ram
4th Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-08), Stanford, CA, October 2008
Case-based planning (CBP) is based on reusing past successful plans for solving new problems. CBP is particularly useful in environments where the large amount of time required to traverse extensive search spaces makes traditional planning techniques unsuitable. In particular, in real-time domains, past plans need to be retrieved and adapted in real time and efficient plan adaptation techniques are required.
We have developed real-time adaptation techniques for case-based planning and specifically applied them to the domain of real-time strategy games. In our framework, when a plan is retrieved, a plan dependency graph is inferred to capture the relations between actions in the plan suggested by that case. The case is then adapted in real-time using its plan dependency graph. This allows the system to create and adapt plans in an efficient and effective manner while performing the task.
Our techniques have been implemented in the Darmok system, designed to play WARGUS, a well-known real-time strategy game. We analyze our approach and prove that the complexity of the plan adaptation stage is polynomial in the size of the plan. We also provide bounds on the final size of the adapted plan under certain assumptions.
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Real-Time Plan Adaptation for Case-Based Planning in Real-Time Strategy Games
by Neha Sugandh, Santi Ontañón, Ashwin Ram
9th European Conference on Case-Based Reasoning (ECCBR-08), Trier, Germany, September 2008