Posts Tagged ‘rts games’

20 Jan

On-Line Case-Based Planning

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

Some domains, such as real-time strategy (RTS) games, pose several challenges to traditional planning and machine learning techniques. In this paper, we present a novel on-line case-based planning architecture that addresses some of these problems. Our architecture addresses issues of plan acquisition, on-line plan execution, interleaved planning and execution and on-line plan adaptation. We also introduce the Darmok system, which implements this architecture in order to play Wargus (an open source clone of the well-known RTS game Warcraft II). We present empirical evaluation of the performance of Darmok and show that it successfully learns to play the Wargus game.

Read the paper:

On-Line Case-Based Planning

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

Computational Intelligence, 26(1):84-119, 2010.
www.cc.gatech.edu/faculty/ashwin/papers/er-09-08.pdf
www3.interscience.wiley.com/journal/123263882/abstract

23 Oct

Stochastic Plan Optimization in Real-Time Strategy Games

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

We present a domain independent off-line adaptation technique called Stochastic Plan Optimization for finding and improving plans in real-time strategy games. Our method is based on ideas from genetic algorithms, but we utilize a different representation for our plans and an alternate initialization procedure for our search process. The key to our technique is the use of expert plans to initialize our search in the most relevant parts of plan space. Our experiments validate this approach using our existing case based reasoning system Darmok in the real-time strategy game Wargus, a clone of Warcraft II.

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Stochastic Plan Optimization in Real-Time Strategy Games

by Andrew Trusty, Santi Ontañón, Ashwin Ram

4th Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-08), Stanford, CA, October 2008
www.cc.gatech.edu/faculty/ashwin/papers/er-08-09.pdf

23 Oct

An Intelligent IDE for Behavior Authoring in Real-Time Strategy Games

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

Behavior authoring for computer games involves writing behaviors in a programming language and then iteratively refining them by detecting issues with them. The main bottlenecks are a) the effort required to author the behaviors and b) the revision cycle as, for most games, it is practically impossible to write a behavior for the computer game AI in a single attempt. The main problem is that the current development environments (IDE) are typically mere text editors that can only help the author by pointing out syntactical errors.

In this paper we present an intelligent IDE (iIDE) that has the following capabilities: it allows the author to program initial versions of the behaviors through demonstration, presents visualizations of behavior execution for revision, lets the author define failure conditions on the existing behavior set, and select appropriate fixes for the failure conditions to correct the behaviors. We describe the underlying techniques that support these capabilities inside our implemented iIDE and the future steps that need to be carried out to improve the iIDE. We also provide details on a preliminary user study showing how the new features inside the iIDE can help authors in behavior authoring and debugging in a real-time strategy game.

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An Intelligent IDE for Behavior Authoring in Real-Time Strategy Games

by Manish Mehta, Suhas Virmani, Yatin Kanetkar, Santi Ontañón, Ashwin Ram

4th Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-08), Stanford, CA, October 2008
www.cc.gatech.edu/faculty/ashwin/papers/er-08-08.pdf

2 Sep

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

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

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.

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

by Neha Sugandh, Santi Ontañón, Ashwin Ram

9th European Conference on Case-Based Reasoning (ECCBR-08), Trier, Germany, September 2008
www.cc.gatech.edu/faculty/ashwin/papers/er-08-06.pdf

2 Sep

Situation Assessment for Plan Retrieval in Real-Time Strategy Games

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

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.

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Situation Assessment for Plan Retrieval in Real-Time Strategy Games

by Kinshuk Mishra, Santi Ontañón, Ashwin Ram

9th European Conference on Case-Based Reasoning (ECCBR-08), Trier, Germany, September 2008
www.cc.gatech.edu/faculty/ashwin/papers/er-08-07.pdf

16 Jul

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

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

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

23rd AAAI Conference on Artificial Intelligence (AAAI-08), Chicago, IL, July 2008
www.cc.gatech.edu/faculty/ashwin/papers/er-08-04.pdf

3 Apr

Case-Based Reasoning for Game AI

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

Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial games. Although games are typically associated with entertainment applications, there are many “serious” applications of gaming, including military, corporate, and advertising applications. There are also what the so called “humane” gaming applications—interactive tools for medical training, educational games, and games that reflect social consciousness or advocate for a cause. Game AI is the effort of taking computer games beyond scripted interactions, however complex, into the arena of truly interactive systems that are responsive, adaptive, and intelligent. Such systems learn about the player(s) during game play, adapt their own behaviors beyond the pre-programmed set provided by the game author, and interactively develop and provide a richer experience to the player(s).

In this talk, I discuss a range of CBR approaches for Game AI. I discuss differences and similarities between character-level AI (in embedded NPCs, for example) and game-level AI (in the drama manager or game director, for example). I explain why the AI must reason at multiple levels, including reactive, tactical, strategic, rhetorical, and meta, and propose a CBR architecture that lets us design and coordinate real-time AIs operating asynchronously at all these levels. I conclude with a brief discussion on the very idea of Game AI: is it feasible? realistic? and would we call it “intelligence” if we could implement all this stuff?

View the talk:

Google Tech Talk: Case-Based Reasoning for Game AI

by Ashwin Ram

Google Tech Talk, Mountain View, CA, April 2008
www.youtube.com/watch?v=s9G7DRTuB5s

20 Feb

Adaptive Computer Games: Easing the Authorial Burden

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

Game designers usually create AI behaviors by writing scripts that describe the reactions to all imaginable circumstances within the confines of the game world. The AI Programming Wisdom series provides a good overview of current scripting techniques used in the game industry. Scripting is expensive and it’s hard to plan. So, behaviors could be repetitive (resulting in breaking the atmosphere) or behaviors could fail to achieve their desired purpose. On one hand, creating AI with a rich behavior set requires a great deal of engineering effort on the part of game developers. On the other hand, the rich and dynamic nature of game worlds makes it hard to imagine and plan for all possible scenarios. When behaviors fail to achieve their desired purpose, the game AI is unable to identify such failure and will continue executing them. The techniques described in this article specifically deal with these issues.

Behavior (or script) creation for computer games typically involves two steps: a) generating a first version of behaviors using a programming language, b) debugging and adapting the behavior via experimentation. In this article we present techniques that aim at assisting the author from carrying out these two steps manually: behavior learning and behavior adaptation.

In the behavior learning process, the game developers can specify the AI behavior by demonstrating it to the system instead of having to code the behavior using a programming language. The system extracts behaviors from these expert demonstrations and stores them. Then, at performance time, the system retrieves appropriate behaviors observed from the expert and revises them in response to the current situation it is dealing with (i.e., to the current game state).

In the behavior adaptation process, the system monitors the performance of these learned behaviors at runtime. The system keeps track of the status of the executing behaviors, infer from their execution trace what might be wrong, and perform appropriate adaptations to the behaviors once the game is over. This approach to behavior transformation enables the game AI to reflect on the issues in the learnt behaviors from expert demonstration and revises them after post analysis of things that went wrong during the game. These set of techniques allow non-AI experts to define behaviors through demonstration that can then be adapted to different situations thereby reducing the development effort required to address all contingencies in a complex game.

Read the paper:

Adaptive Computer Games: Easing the Authorial Burden

by Manish Mehta, Santi Ontañón, Ashwin Ram

AI Game Programming Wisdom 4 (AIGPW4), Steve Rabin (editor), Charles River Media, 2008
www.cc.gatech.edu/faculty/ashwin/papers/er-08-03.pdf

16 Aug

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

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

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

Seventh International Conference on Case-Based Reasoning (ICCBR-07), Belfast, Northern Ireland, August 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-11.pdf

8 May

Artificial Intelligence for Adaptive Computer Games

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

Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial games. Although games are typically associated with entertainment, there are many “serious” applications of gaming, including military, corporate, and advertising applications. There are also so-called “humane” gaming applications for medical training, educational games, and games that reflect social consciousness or advocate for a cause. Game AI is the effort of going beyond scripted interactions, however complex, into the arena of truly interactive systems that are responsive, adaptive, and intelligent. Such systems learn about the player(s) during game play, adapt their own behaviors beyond the pre-programmed set provided by the game author, and interactively develop and provide a richer experience to the player(s).

The long-term goal of our research is to develop artificial intelligence techniques that can have a significant impact in the game industry. We present a list of challenges and research opportunities in developing techniques that can be used by computer game developers. We discuss three Case Based Reasoning (CBR) approaches to achieve adaptability in games: automatic behavior adaptation for believable characters; drama management and user modeling for interactive stories; and strategic behavior planning for real-time strategy games.

Read the paper:

Artificial Intelligence for Adaptive Computer Games

by Ashwin Ram, Santi Ontañón, Manish Mehta

Invited talk at the 20th International FLAIRS Conference on Artificial Intelligence (FLAIRS-07), Special Track on Case-Based Reasoning, Key West, FL, May 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-04.pdf
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