Posts Tagged ‘case-based reasoning’

20 Jul

MMPM: A Generic Platform for Case-Based Planning Research

Posted by cognitivecomputing in Game AI. Tagged: , , , .

Computer games are excellent domains for research and evaluation of AI and CBR techniques. The main drawback is the effort needed to connect AI systems to existing games. This paper presents MMPM, a middleware platform that supports easy connection of AI techniques with games. We will describe the MMPM architecture, and compare with related systems such as TIELT.

Read the paper:

MMPM: A Generic Platform for Case-Based Planning Research

by Pedro Pablo Gómez-Martín, David Llansó, Marco Antonio Gómez-Martín, Santiago Ontañón, Ashwin Ram

ICCBR-2010 Workshop on Case-Based Reasoning for Computer Games
www.cc.gatech.edu/faculty/ashwin/papers/er-10-03.pdf

19 Jul

Real-Time Case-Based Reasoning for Interactive Digital Entertainment

Posted by cognitivecomputing in Agents, Game AI, Learning, Talks, Web / Web 2.0. Tagged: , , , , , , , , , .

(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

Meta-Level Behavior Adaptation in Real-Time Strategy Games

Posted by cognitivecomputing in Agents, Game AI, Learning. Tagged: , , , , , , .

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

10 Jul

Conversational Framework for Web Search and Recommendations

Posted by cognitivecomputing in Agents, Health & Wellness, Language, Web / Web 2.0. Tagged: , , , , , .

We introduce a Conversational Interaction framework as an innovative and natural approach to facilitate easier information access by combining web search and recommendations. This framework includes an intelligent information agent (Cobot) in the conversation to provide contextually relevant social and web search recommendations. Cobot supports the information discovery process by integrating web information retrieval along with proactive connections to relevant users who can participate in real-time conversations. We describe the conversational framework and report on some preliminary experiments in the system.

Read the paper:

Conversational Framework for Web Search and Recommendations

by Saurav Sahay, Ashwin Ram

ICCBR-10 Workshop on Reasoning from Experiences on the Web (WebCBR-10), Alessandria, Italy, 2010.
www.cc.gatech.edu/faculty/ashwin/papers/er-10-01.pdf

22 Apr

ICCBR-10 Workshop on CBR Startups

Posted by cognitivecomputing in Opinion, Talks. Tagged: .

CBR Startups

ICCBR 2010 Workshop / July 20, 2010 / Alessandria, Italy

Please fill out a short participation survey: www.surveymonkey.com/s/S2ST588

Over the past twenty-five years, Case-Based Reasoning has matured into a full-fledged discipline within AI, with an international community, strong research momentum, and many commercial successes. However, despite its many advantages as a technology, CBR is not well known in the entrepreneurial world. In part, this is due to few startups being created by CBR researchers, who are the best people to initiate commercialization of their ideas.

This workshop will focus on the merits and challenges of creating a technology startup out of cutting-edge research in academia or research labs. We will discuss technological issues, such as the application areas best suited for CBR approaches and scalability of CBR technologies. We will also discuss practical issues, such as the tension between academic goals (e.g., publishing papers) and commercialization goals (e.g., building applications), and the different types of expertise required to create a vision (researchers), market a product (marketers), and build a company (entrepreneurs).

In true CBR fashion, we will use cases to tackle these issues.  We will hear from CBR researchers who have created CBR startups, and use their experiences to discuss different ways to commercialize CBR technologies. Some have chosen a hands-on approach, taking on a management role (CEO) or a technology role (CTO). Others have partnered with experienced business people, who have taken their ideas forward. We will also provide a forum to help participants with their startup ideas.

Agenda/Schedule

CBR Startups will be a half-day workshop with short talks by people who have spun out companies from their universities, a panel discussion with open audience questions on the merits and challenges of doing a startup, alternative ways of commercializing CBR technologies, and advice to people interested in doing this.

We will conclude with a hands-on session with 3 minute pitches by participants. Think of it as throwing down the gauntlet—a friendly competition where you pitch your CBR startup idea. Prizes will be awarded for most innovative use of CBR technology, best business idea, and idea most likely to succeed. Our intention is to provide advice and mentoring by community members who have been-there-done-that, using these ideas as case studies for all of us to learn from.

Tentative agenda:

  • Introduction and context (Ashwin Ram)
  • Short talks by CBR researchers who have done startups
  • Panel discussion with open audience questions
  • 3 minute CBR gauntlet Pitch competition

We want this to be useful to you, so please help us refine the agenda by filling out this brief survey: www.surveymonkey.com/s/S2ST588

Organizers

Invited Speakers & Mentors

[More to come. If you’d like to speak or mentor, please fill out the survey.]

Help us publicize this workshop!

Please forward this URL to others who might be interested: http://bit.ly/cbr-startups

For more information about ICCBR 2010, see: www.iccbr.org/iccbr10

28 Sep

User-Generated AI for Interactive Digital Entertainment

Posted by cognitivecomputing in Agents, Game AI, Learning, Talks, Web / Web 2.0. Tagged: , , , , , , , , , , .

CMU Seminar

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:

19 Sep

Drama Management and Player Modeling for Interactive Fiction Games

Posted by cognitivecomputing in Game AI. Tagged: , , , , .

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

22 Jul

Using Meta-Reasoning to Improve the Performance of Case-Based Planning

Posted by cognitivecomputing in Game AI, Learning. Tagged: , , , , , .

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

Authoring Behaviors for Games using Learning from Demonstration

Posted by cognitivecomputing in Game AI, Learning. Tagged: , , , .

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

An Ensemble Learning and Problem Solving Architecture for Airspace Management

Posted by cognitivecomputing in Learning. Tagged: , , , .

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
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