Adaptive AI Engine for RTS Games

Discussing the theory and practice

On-Line Case-Based Plan Adaptation for RTS Games

Posted by Ahmad Atta on October 22, 2009

Abstract

After reading about Case-based Reasoning, the second step we should talk is understanding Case-based planning, my reference in this post will be the paper of Neha Sugandh, Santiago Ontanon, and Ashwin Ram titled: On-Line Case-Based Plan Adaptation for Real-Time Strategy Games. This paper presents techniques allow the system to create and adapt plans in an efficient and effective manner while performing the task.

Case-Based Planning in WARGUS

In this section we will briefly describe the Darmok system, which applies the plan adaptation techniques. Darmok was designed to play the game of WARGUS . In order to achieve this, Darmok learns behaviors from expert demonstrations, and then uses case-based planning to play the game reusing the learnt behaviors. Figure 2 shows an overview of our case-based planning approach.

Darmok System

Basically, we divide the process in two main stages:

• Behavior acquisition:

During this first stage, an expert plays a game of WARGUS and the trace of that game is stored. Then, the expert annotates the trace explaining the goals he was pursuing with the actions he took while playing. Using those annotations, a set of behaviors are extracted from the trace and stored as a set of cases.

Behavior Execution:

The execution engine consists of several modules that together maintain a current plan to win the game.

  1. The Plan Execution module: Executes the current plan, and updates its state (marking which actions succeeded or failed).
  2. The Plan Expansion module: Identifies open goals(a goal still doesn’t have an assigned behavior) in the current plan and expands them.
  3. The Behavior Retrieval module: Given an open goal and the current game state, it retrieves the most appropriate behavior to fulfill the open goal.
  4. The Plan Adaptation module (the focus of this paper): Adapts the retrieved plans according to the current game state.

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