#case-based reasoning

[theiteducation]

Expert System - An Area of Artificial intelligence

Expert System – An Area of Artificial intelligence

Expert Systems are special research area of Artificial intelligence. These slides on Expert System in the form of PDF format are made and published for the students computer sciences and Information technology  of undergraduate level. Student may download for study reference. Almost in all universities Artificial intelligence is being taught in CS and IT discipline in last year of the classes.…

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[mayurishahane-blog]

Case-Based Reasoning Systems Market - Global Case-Based Reasoning Systems Industry Size, Share,Trends,Global Market Estimates, Forecasts and Research Report

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Request for a sample copy of the ‘Case-Based Reasoning Systems’ Market report@:  https://www.globalmarketestimates.com/case-based-reasoning-systems-market/

[hackthechad]

Case-Based Reasoning AI Research Paper is Available

If anyone wants a online copy of the Case-Based Reasoning/Reinforcement Learning Starcraft AI paper a colleague and I wrote--it has been published! You can find it at the ICCBR 2010 home page or click this link (it is a large PDF, so be patient...). Enjoy!

[hackthechad]

Case-Based Reasoning AI Research Updates

If anyone is wondering what ever happened to my CBR AI Research, it is being presented tomorrow at the ICCBR 2010 conference in Alessandria, Italy. If you hop on a plane right now you might get there in time to see it! Here is the CBR Workshop for Computer Games schedule: ICCBR Conference 2010 Website

TimeTopic09:15 - 09:30 Introduction and Goals of the workshop09:30 - 10:10 Invited Talk: Artificial Intelligence for Games: Challenges and Opportunities Pedro A. Gonzalez Calero 10:10 - 10:30 Using Automated Replay Annotation for Case-Based Planning in Games Ben Weber and Santiago Ontañón10:30 - 10:45 Coffee Break10:45 - 11:05 Toward a Domain-independent Case-based Reasoning Approach for Imitation: Three Case Studies in Gaming Michael Floyd and Babak Esfandiari11:05 - 11:25 Feature Selection for CBR in Imitation of RoboCup Agents: A Comparative Study Edgar Raul Acosta Villaseñor, Babak Esfandiari and Michael W. Floyd11:25 - 11:45 Meta-Level Behavior Adaptation in Real-Time Strategy Games Manish mehta, Santi Ontanon and Ashwin Ram11:45 - 12:05 Online Micro-Level Decision Making in Real-Time Strategy Games: A Case-Based Reasoning and Reinforcement Learning Approach Chad Mowery, Nathan Spencer and Isabelle Bichindaritz12:05 - 12:25 MMPM: a Generic Platform for Case-Based Planning Research Pedro-Pablo Gómez Martín, David LLansó, Marco Antonio Gómez Martín, Santiago Ontañón and Ashwin Ram12:25 - 12:30 Closing Remarks

Also, if you are a Twitter user you can follow the conference progress using: #iccbr. OR you can be lazy and click this link to go there from here. I will post more about my research on this topic after it has been printed.

[hackthechad]

Publication Paper Abstract

I am currently working on finishing a publication paper on the work a colleague and I have done on Case-Based Reasoning coupled with Machine Learning in RTS games. Here is an early version of the abstract preceding the paper:

Online Micro-Level Decision Making in Real-Time Strategy Games Using Case-Based Reasoning and Reinforcement Learning Systems 

(Tentative Title)

Chad Mowery, Nathan Spencer

Department of Computing and Software Systems

University of Washington, Tacoma

{mowerych, nathanps}@u.washington.edu

Abstract. The specialization of artificial intelligence (AI) techniques within the gaming industry is becoming a fast growing field of study for students in academia and industry developers alike. Many of the AI techniques used in modern video games are unique to a gaming problem domain as game developers often face the challenge of implementing their game AI with limited resources (game frames, memory, and processor cycles), more so than traditional AI projects and studies. However, due to the technological advancement of modern video game consoles and PCs, previous AI techniques used in industry are quickly becoming outdated and uninteresting to players. In this paper we outline a complete AI system that utilizes Case-based Reasoning and Reinforcement Learning systems to provide new levels of player immersion. By utilizing real-time learning with Case-based Reasoning we can give a new outlook on games artificial intelligence as challenging and adaptive opponents for players.

[hackthechad]

Case-Based Reasoning and Reinforcement Learning in Real-Time Strategy Games.

I just gave a presentation on using Case-Based Reasoning and Reinforcement Learning in RTS games to facilitate micro-level decisions back on the 12th at the University of Washington Tacoma campus, so I thought I would share some of the AI concepts used in my work. Game: Starcraft with the Brood War expansion (Blizzard TM). Tech: BWAPI Which is a lovely library that provides hooks into Starcraft and allows you to provide a custom AI dll to inject into the game at runtime using just about any programming language you prefer (I used C++). Find it here: http://code.google.com/p/bwapi/ So what is Case-Based Reasoning (CBR)? CBR is a significantly different approach to solving problems when compared to more traditional AI techniques such as decision trees, state-based machines, and various expert systems. That is, CBR records and uses specific knowledge from previously experienced problem situations. Thus, problems are solved by recalling these previous experiences (cases) and applying them in new the new problem context. This kind of problem solving is very interesting as there are many strong similarities with our own natural cognitive abilities. We (humans) often learn by example and experience. The recall, adaptation, and application of these experiences is almost identical to the fundamental processes in CBR. And what about Reinforcement Learning (RL)? RL is used as a means for our AI to adapt it's experiences (cases) and make certain generalizations about them in order to extend their usage across larger problem situation domains. This way, our AI must gain experience by playing games of Starcraft so that it can learn how to solve problem situations during game-play. Additionally, it also has to learn  how to play a game of Starcraft (i.e. what it means to win and how it must go about doing this). RL simply uses reward and punishment functions following a feedback loop during a game in order to provide a positive or negative reinforcement to a recalled case in hopes to further refine the solution contained within it. I don't claim to be an expert on either of these topics as I am still learning about them through this project. If you want more in-depth information here are a few links: http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/CaseBasedReasoning http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/MachineLearning

[davidrussellschilling]

DARPA Behavior Learning for Adaptive Electronic Warfare (BLADE)

DARPA Behavior Learning for Adaptive Electronic Warfare (BLADE)

C O N T E N T S: KEY TOPICS The Behavioral Learning for Adaptive Electronic Warfare (BLADE) program is developing the capability to counter new and dynamic wireless communication threats in tactical environments.(More…) The Adaptive RF Technology (ART) program aims to significantly advance the hardware used in communication radios by developing a fully adaptive and reconfigurable architecture…

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Case-Based Reasoning in Games

Case-based reasoning (CBR) is an artificial intelligence approach based on the idea that a new problem can be solved by adapting a solution from previously experienced similar problem. In order for this approach to work a collection of previously experienced problems with corresponding solutions (cases) is required. Each time a new problem arises the most similar problem is retrieved from this collection and its solution is modified to fit the new problem.