Cognitive Computing A research blog by Ashwin Ram, PhD

Case-based planning (CBP) is based on reusing past successful plans for solving new problems. CBP is particularly useful in environments where the large amount of time required to traverse extensive search spaces makes traditional planning techniques unsuitable. In particular, in real-time domains, past plans need to be retrieved and adapted in real time and efficient plan adaptation techniques are required.

We have developed real-time adaptation techniques for case-based planning and specifically applied them to the domain of real-time strategy games. In our framework, when a plan is retrieved, a plan dependency graph is inferred to capture the relations between actions in the plan suggested by that case. The case is then adapted in real-time using its plan dependency graph. This allows the system to create and adapt plans in an efficient and effective manner while performing the task.

Our techniques have been implemented in the Darmok system, designed to play WARGUS, a well-known real-time strategy game. We analyze our approach and prove that the complexity of the plan adaptation stage is polynomial in the size of the plan. We also provide bounds on the final size of the adapted plan under certain assumptions.

Read the paper:

Real-Time Plan Adaptation for Case-Based Planning in Real-Time Strategy Games

by Neha Sugandh, Santi Ontañón, Ashwin Ram

www.cc.gatech.edu/faculty/ashwin/papers/er-08-06.pdf

Case-Based Planning (CBP) is an effective technique for solving planning problems that has the potential to reduce the computational complexity of the generative planning approaches. However, the success of plan execution using CBP depends highly on the selection of a correct plan; especially when the case-base of plans is extensive.

In this paper we introduce the concept of a situation and explain a situation assessment algorithm which improves plan retrieval for CBP. We have applied situation assessment to our previous CBP system, Darmok, in the domain of real-time strategy games. During Darmok’s execution using situation assessment, the high-level representation of the game state i.e. situation is predicted using a decision tree based Situation-Classification model. Situation predicted is further used for the selection of relevant knowledge intensive features, which are derived from the basic representation of the game state, to compute the similarity of cases with the current problem. The feature selection performed here is knowledge-based and improves the performance of similarity measurements during plan retrieval. The instantiation of the situation assessment algorithm to Darmok gave us promising results for plan retrieval within the real-time constraints.

Read the paper:

Situation Assessment for Plan Retrieval in Real-Time Strategy Games

by Kinshuk Mishra, Santi Ontañón, Ashwin Ram

www.cc.gatech.edu/faculty/ashwin/papers/er-08-07.pdf

Traditional artificial intelligence techniques do not perform well in applications such as real-time strategy games because of the extensive search spaces which need to be explored. In addition, this exploration must be carried out on-line during performance time; it cannot be precomputed. We have developed on-line case-based planning techniques that are effective in such domains. In this paper, we extend our earlier work using ideas from traditional planning to inform the real-time adaptation of plans. In our framework, when a plan is retrieved, a plan dependency graph is inferred to capture the relations between actions in the plan. The plan is then adapted in real-time using its plan dependency graph. This allows the system to create and adapt plans in an efficient and effective manner while performing the task. The approach is evaluated using WARGUS, a well-known real-time strategy game.

Read the paper:

On-Line Case-Based Plan Adaptation for Real-Time Strategy Games

by Neha Sugandh, Santi Ontañón, Ashwin Ram

www.cc.gatech.edu/faculty/ashwin/papers/er-08-04.pdf

Artificial Intelligence techniques have been successfully applied to several computer games. However in some kinds of computer games, like real-time strategy (RTS) games, traditional artificial intelligence techniques fail to play at a human level because of the vast search spaces that they entail. In this paper we present a real-time case based planning and execution approach designed to deal with RTS games. We propose to extract behavioral knowledge from expert demonstrations in form of individual cases. This knowledge can be reused via a case based behavior generator that proposes behaviors to achieve the specific open goals in the current plan. Specifically, we applied our technique to the WARGUS domain with promising results.

Read the paper:

Case-Based Planning and Execution for Real-Time Strategy Games

by Santi Ontañón, Kinshuk Mishra, Neha Sugandh, Ashwin Ram

www.cc.gatech.edu/faculty/ashwin/papers/er-07-11.pdf