Archive for the ‘Learning’ Category
25
Feb
Posted by cognitivecomputing in Game AI, Learning. Tagged: case-based reasoning, games, real-time cbr, rts games. 2 comments
Creating AI for complex computer games requires a great deal of technical knowledge as well as engineering effort on the part of game developers. This paper focuses on techniques that enable end-users to create AI for games without requiring technical knowledge by using case-based reasoning techniques.
AI creation for computer games typically involves two steps: a) generating a first version of the AI, and b) debugging and adapting it via experimentation. We will use the domain of real-time strategy games to illustrate how case-based reasoning can address both steps.
Read the paper:
Case-Based Reasoning and User-Generated AI for Real-Time Strategy Games
by Santi Ontañón and Ashwin Ram
In P. Gonzáles-Calero & M. Gomez-Martín (ed.), AI for Games: State of the Practice, 2011.
www.cc.gatech.edu/faculty/ashwin/papers/er-11-02.pdf
24
Feb
Posted by cognitivecomputing in Agents, Game AI, Learning. Leave a comment
In a competitive game it is important to identify the opponent’s strategy as quickly and accurately as possible so that an effective response can be staged. In this vein, this paper summarizes our work in exploring the use of the first order inductive learning (FOIL) algorithm for learning rules which can be used to represent opponent strategies. Specifically, we use these learned rules to perform plan recognition and classify an opponent strategy as one of multiple learned strategies. Our experiments validate this novel approach in a simple real-time strategy game.
Read the paper:
Learning Opponent Strategies through First Order Induction
by Kathryn Genter, Santiago Ontañón, Ashwin Ram
FLAIRS-11 International Conference on Artificial Intelligence
www.cc.gatech.edu/faculty/ashwin/papers/er-11-03.pdf
19
Jul
Posted by cognitivecomputing in Agents, Game AI, Learning, Talks, Web / Web 2.0. Tagged: believable agents, case-based reasoning, games, goal-driven learning, interactive drama, meta-reasoning, problem solving, real-time cbr, rts games, virtual worlds. 1 comment
(Click image to view the video – it’s near the bottom of the new page.)
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 authoring 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 will 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 propose an alternative approach to designing Game AI: Real-Time CBR. This approach extends CBR to real-time systems that operate asynchronously during game play, planning, adapting, and learning in an online manner. Originally developed for robotic control, Real-Time CBR can be used for interactive games ranging from multiplayer strategy games to interactive believable avatars in virtual worlds.
As with any CBR technique, Real-Time CBR integrates problem solving with learning. This property can be used to address the authoring problem. I will 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 role of CBR in AI-based Interactive Digital Entertainment.
Keynote talk at the Eighteenth Conference on Pattern Recognition and Artificial Intelligence (RFIA-12), Lyon, France, February 5, 2012.
Slides and video here: rfia2012.liris.cnrs.fr/doku.php?id=pub:ram
Keynote talk at the Eleventh Scandinavian Conference on Artificial Intelligence (SCAI-11), Trondheim, Norway, May 25, 2011.
Keynote talk at the 2010 International Conference on Case-Based Reasoning (ICCBR-10), Alessandria, Italy, July 22, 2010.
GVU Brown Bag talk, October 14, 2010. Watch the talk here: www.gvu.gatech.edu/node/4320
Try it yourself:
Learn more about the algorithms:
View the talk:
www.sais.se/blog/?p=57
View the slides:
10
Jul
Posted by cognitivecomputing in Agents, Game AI, Learning. Tagged: believable agents, case-based reasoning, games, goal-driven learning, meta-reasoning, real-time cbr, rts games. Leave a comment
AI agents designed for real-time settings need to adapt themselves to changing circumstances to improve their performance and remedy their faults. Agents typically designed for computer games, however, lack this ability. The lack of adaptivity causes a break in player experience when they repeatedly fail to behave properly in circumstances unforeseen by the game designers.
We present an AI technique for game-playing agents that helps them adapt to changing game circumstances. The agents carry out runtime adaptation of their behavior sets by monitoring and reasoning about their behavior execution and using this reasoning to dynamically revise their behaviors. The evaluation of the behavior adaptation approach in a complex real-time strategy game shows that the agents adapt themselves and improve their performance by revising their behavior sets appropriately.
Read the paper:
Meta-Level Behavior Adaptation in Real-Time Strategy Games
by Manish Mehta, Santi Ontañon, Ashwin Ram
ICCBR-10 Workshop on Case-Based Reasoning for Computer Games, Alessandria, Italy, 2010.
www.cc.gatech.edu/faculty/ashwin/papers/er-10-02.pdf
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
Try it yourself:
Learn more about the algorithms:
View the talk:
www.sais.se/blog/?p=57
View the slides:
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
16
Jul
Posted by cognitivecomputing in Learning. Tagged: case-based reasoning, meta-reasoning, multistrategy learning, problem solving. Leave a comment
In this paper we describe the application of a novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspace need to be reconciled and managed automatically. The key feature of our “Generalized Integrated Learning Architecture” (GILA) is a set of integrated learning and reasoning (ILR) systems coordinated by a central meta-reasoning executive (MRE). Each ILR learns independently from the same training example and contributes to problem-solving in concert with other ILRs as directed by the MRE. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Further, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.
Read the paper:
An Ensemble Learning and Problem Solving Architecture for Airspace Management
by XS Zhang et al.
International Conference on Innovative Applications of Artificial Intelligence (IAAI-09), Pasadena, CA, July 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-09-03.pdf
15
Jul
Posted by cognitivecomputing in Learning. Tagged: goal-driven learning, meta-reasoning, multistrategy learning, problem solving. Leave a comment
Goal Driven Learning (GDL) focuses on systems that determine by themselves what has to be learned and how to learn it. Typically GDL systems use meta-reasoning capabilities over a base reasoner, identifying learning goals and devising strategies. In this paper we present a novel GDL technique to deal with complex AI systems where the meta-reasoning module has to analyze the reasoning trace of multiple components with potentially different learning paradigms. Our approach works by distributing the generation of learning strategies among the different modules instead of centralizing it in the meta-reasoner. We implemented our technique in the GILA system, that works in the airspace task orders domain, showing an increase in performance.
Read the paper:
Goal-Driven Learning in the GILA Integrated Intelligence Architecture
by Jai Radhakrishnan, Santi Ontañón, Ashwin Ram
International Joint Conference on Artificial Intelligence (IJCAI-09), Pasadena, CA, July 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-09-02.pdf
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