ISDA’10 “Intelligent OLCBP Agent Model for RTS Games” Presentation


Today I gave a presentation in ISDA’10 about the recently published paper “Intelligent OLCBP Agent Model ofr RTS Games”. Below is this presentation !

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My First Research Paper ! (To Be Published)


Greetings ! I wanted to share with you my first AI-related paper which will be published soon. I might (or maybe not) upload the whole paper in another post later to gain your reviews, but for now, I’m showing the abstract and keywords.

Abstract

Research in learning and planning in real-time  strategy (RTS) games is  very  interesting  in  several industries  such as military industry, robotics,  and most importantly game  industry.    A  Recent  published  work on online  case-based  planning in RTS Games does not include the capability of online  learning from experience, so the knowledge certainty remains  constant,  which leads to inefficient decisions. In this  paper,  an  intelligent agent model based on both online case-based planning  (OLCBP)  and reinforcement learning  (RL)  techniques  is  proposed.  In addition, the proposed model has been evaluated  using empirical simulation on Wargus (an open-source clone of  the well known Real-Time Strategy Game Warcraft 2).   This  evaluation shows that the proposed model increases the certainty  of the  case  base  by learning from experience, and hence the  process of decision making  for selecting more efficient, effective  and successful plans.

Keywords

Case-based  reasoning Reinforcement  LearningOnline  Case-based  Planning, Real-Time Strategy Games,  Sarsa (λ) learning, Eligibility Traces, Intelligent Agent.