Archive for the ‘Game AI’ Category
28
Sep
Posted by cognitivecomputing in Agents, Game AI, Learning, Talks, Web / Web 2.0. Tagged: believable agents, case-based reasoning, games, interactive drama, meta-reasoning, multistrategy learning, planning, problem solving, real-time cbr, rts games, virtual worlds. 3 comments
User-generated content is everywhere: photos, videos, news, blogs, art, music, and every other type of digital media on the Social Web. Games are no exception. From strategy games to immersive virtual worlds, game players are increasingly engaged in creating and sharing nearly all aspects of the gaming experience: maps, quests, artifacts, avatars, clothing, even games themselves. Yet, there is one aspect of computer games that is not created and shared by game players: the AI. Building sophisticated personalities, behaviors, and strategies requires expertise in both AI and programming, and remains outside the purview of the end user.
To understand why Game AI is hard, we need to understand how it works. AI can take digital entertainment beyond scripted interactions into the arena of truly interactive systems that are responsive, adaptive, and intelligent. I discuss examples of AI techniques for character-level AI (in embedded NPCs, for example) and game-level AI (in the drama manager, for example). These types of AI enhance the player experience in different ways. The techniques are complicated and are usually implemented by expert game designers.
I argue that User-Generated AI is the next big frontier in the rapidly growing Social Gaming area. From Sims to Risk to World of Warcraft, end users want to create, modify, and share not only the appearance but the “minds” of their characters. I present my recent research on intelligent technologies to assist Game AI authors, and show the first Web 2.0 application that allows average users to create AIs and challenge their friends to play them—without programming. I conclude with some thoughts about the future of AI-based Interactive Digital Entertainment.
CMU Robotics & Intelligence Seminar, September 28, 2009
Carnegie-Mellon University, Pittsburgh, PA.
MIT Media Lab Colloquium, January 25, 2010
Massachusetts Institute of Technology, Cambridge, MA.
Stanford Media X Philips Seminar, February 1, 2010
Stanford University, Stanford, CA.
Pixar Research Seminar, February 2, 2010
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www.sais.se/blog/?p=57
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19
Sep
Posted by cognitivecomputing in Game AI. Tagged: believable agents, case-based reasoning, games, interactive drama, problem solving. 2 comments
A growing research community is working towards employing drama management components in story-based games. These components gently guide the story towards a narrative arc that improves the player’s gaming experience. In this paper we evaluate a novel drama management approach deployed in an interactive fiction game called Anchorhead. This approach uses player’s feedback as the basis for guiding the personalization of the interaction.
The results indicate that adding our Case-based Drama manaGer (C-DraGer) to the game guides the players through the interaction and provides a better overall player experience. Unlike previous approaches to drama management, this paper focuses on exhibiting the success of our approach by evaluating results using human players in a real game implementation. Based on this work, we report several insights on drama management which were possible only due to an evaluation with real players.
Read the paper:
Drama Management and Player Modeling for Interactive Fiction Games
by Manu Sharma, Santi Ontañón, Manish Mehta, Ashwin Ram
Computational Intelligence, 26(2):183-211, 2010.
www.cc.gatech.edu/faculty/ashwin/papers/er-09-10.pdf
www3.interscience.wiley.com/journal/123387570/abstract
9
Sep
Posted by cognitivecomputing in Game AI, Learning. Tagged: believable agents, games, goal-driven learning, meta-reasoning, planning, real-time cbr, virtual worlds. Leave a comment
Intelligent agents working in real-time domains need to adapt to changing circumstance so that they can improve their performance and avoid their mistakes. AI agents designed for interactive games, however, typically lack this ability. Game agents are traditionally implemented using static, hand-authored behaviors or scripts that are brittle to changing world dynamics and cause a break in player experience when they repeatedly fail. Furthermore, their static nature causes a lot of effort for the game designers as they have to think of all imaginable circumstances that can be encountered by the agent. The problem is exacerbated as state-of-the-art computer games have huge decision spaces, interactive user input, and real-time performance that make the problem of creating AI approaches for these domains harder.
In this paper we address the issue of non-adaptivity of game playing agents in complex real-time domains. The agents carry out run-time adaptation of their behavior sets by monitoring and reasoning about their behavior execution to dynamically carry out revisions on the behaviors. The behavior adaptation approaches has been instantiated in two real-time interactive game domains. The evaluation results shows that the agents in the two domains successfully adapt themselves by revising their behavior sets appropriately.
Read the paper:
Run-Time Behavior Adaptation for Real-Time Interactive Games
by Manish Mehta, Ashwin Ram
IEEE Transactions on Computational Intelligence and AI in Games, Vol. 1, No. 3, September 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-09-09.pdf
22
Jul
Posted by cognitivecomputing in Game AI, Learning. Tagged: case-based reasoning, games, meta-reasoning, planning, real-time cbr, rts games. 1 comment
Case-based planning (CBP) systems are based on the idea of reusing past successful plans for solving new problems. Previous research has shown the ability of meta-reasoning approaches to improve the performance of CBP systems. In this paper we present a new meta-reasoning approach for autonomously improving the performance of CBP systems that operate in real-time domains.
Our approach uses failure patterns to detect anomalous behaviors, and it can learn from experience which of the failures detected are important enough to be fixed. Finally, our meta-reasoning approach can exploit both successful and failed executions for meta-reasoning.
We illustrate its benefits with experimental results from a system implementing our approach called Meta-Darmok in a real-time strategy game. The evaluation of Meta-Darmok shows that the system successfully adapts itself and its performance improves through appropriate revision of the case base.
Read the paper:
Using Meta-Reasoning to Improve the Performance of Case-Based Planning
by Manish Mehta, Santi Ontañón, Ashwin Ram
International Conference on Case-Based Reasoning (ICCBR-09), Seattle, July 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-09-06.pdf
21
Jul
Posted by cognitivecomputing in Game AI, Learning. Tagged: case-based reasoning, games, interactive drama, rts games. Leave a comment
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.
Read the paper:
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
www.cc.gatech.edu/faculty/ashwin/papers/er-09-07.pdf
12
Jul
Posted by cognitivecomputing in Game AI, Learning. Tagged: case-based reasoning, games, planning, real-time cbr, rts games. 2 comments
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.
Read the paper:
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
www.cc.gatech.edu/faculty/ashwin/papers/er-09-04.pdf
15
May
Posted by cognitivecomputing in Game AI, Learning. Tagged: games, goal-driven learning, rts games. 3 comments
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.
Read the thesis:
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
www.cs.utexas.edu/users/katie/UgradThesis.pdf
11
May
Posted by cognitivecomputing in Game AI. Tagged: believable agents, games, interactive drama, virtual worlds. Leave a comment
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.
Read the paper:
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
www.cc.gatech.edu/faculty/ashwin/papers/er-09-01.pdf
20
Jan
Posted by cognitivecomputing in Game AI, Learning. Tagged: case-based reasoning, games, planning, real-time cbr, rts games. 3 comments
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.
Read the paper:
On-Line Case-Based Planning
by Santi Ontañón, Neha Sugandh, Kinshuk Mishra, Ashwin Ram
Computational Intelligence, 26(1):84-119, 2010.
www.cc.gatech.edu/faculty/ashwin/papers/er-09-08.pdf
www3.interscience.wiley.com/journal/123263882/abstract
26
Nov
Posted by cognitivecomputing in Agents, Game AI. Tagged: games, interactive drama. Leave a comment
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
www.cc.gatech.edu/faculty/ashwin/papers/er-08-11.pdf
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