Posts Tagged ‘planning’
22
Jul
Posted by cognitivecomputing in Game AI, Learning. Tagged: case-based reasoning, games, meta-reasoning, planning, real-time cbr, rts games. 1 comment
Case-based planning (CBP) systems are based on the idea of reusing past successful plans for solving new problems. Previous research has shown the ability of meta-reasoning approaches to improve the performance of CBP systems. In this paper we present a new meta-reasoning approach for autonomously improving the performance of CBP systems that operate in real-time domains.
Our approach uses failure patterns to detect anomalous behaviors, and it can learn from experience which of the failures detected are important enough to be fixed. Finally, our meta-reasoning approach can exploit both successful and failed executions for meta-reasoning.
We illustrate its benefits with experimental results from a system implementing our approach called Meta-Darmok in a real-time strategy game. The evaluation of Meta-Darmok shows that the system successfully adapts itself and its performance improves through appropriate revision of the case base.
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Using Meta-Reasoning to Improve the Performance of Case-Based Planning
by Manish Mehta, Santi Ontañón, Ashwin Ram
International Conference on Case-Based Reasoning (ICCBR-09), Seattle, July 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-09-06.pdf
12
Jul
Posted by cognitivecomputing in Game AI, Learning. Tagged: case-based reasoning, games, planning, real-time cbr, rts games. 2 comments
One of the main bottlenecks in deploying case-based planning systems is authoring the case-base of plans. In this paper we present a collection of algorithms that can be used to automatically learn plans from human demonstrations. Our algorithms are based on the basic idea of a plan dependency graph, which is a graph that captures the dependencies among actions in a plan. Such algorithms are implemented in a system called Darmok 2 (D2), a case-based planning system capable of general game playing with a focus on real-time strategy (RTS) games. We evaluate D2 with a collection of three different games with promising results.
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Learning from Human Demonstrations for Real-Time Case-Based Planning
by Santi Ontañón, Kane Bonnette, Praful Mahindrakar, Marco Gómez-Martin, Katie Long, Jai Radhakrishnan, Rushabh Shah, Ashwin Ram
IJCAI-09 Workshop on Learning Structural Knowledge from Observations, Pasadena, CA, July 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-09-04.pdf
20
Jan
Posted by cognitivecomputing in Game AI, Learning. Tagged: case-based reasoning, games, planning, real-time cbr, rts games. 3 comments
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.
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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
Posted by cognitivecomputing in Game AI. Tagged: case-based reasoning, games, planning, rts games. 1 comment
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
2
Sep
Posted by cognitivecomputing in Game AI. Tagged: case-based reasoning, games, planning, real-time cbr, rts games. Leave a comment
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
Posted by cognitivecomputing in Game AI. Tagged: case-based reasoning, games, planning, real-time cbr, rts games. Leave a comment
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
Posted by cognitivecomputing in Game AI. Tagged: case-based reasoning, games, planning, real-time cbr, rts games. Leave a comment
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.
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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
16
Aug
Posted by cognitivecomputing in Game AI. Tagged: case-based reasoning, games, planning, real-time cbr, rts games. 1 comment
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.
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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
9
May
Posted by cognitivecomputing in Game AI, Language. Tagged: case-based reasoning, games, interactive drama, planning. Leave a comment
There is a growing interest in producing story based game experiences that do not follow fixed scripts pre-defined by the author, but change the experience based on actions performed by the player during his interaction. In order to achieve this objective, previous approaches have employed a drama management component that produces a narratively pleasing arc based on an author specified aesthetic value of a story, ignoring a player’s personal preference for that story path. Furthermore, previous approaches have used a simulated player model to assess their approach, ignoring real human players interacting with the story-based game.
This paper presents an approach that uses a case-based player preference modeling component that predicts an interestingness value for a particular plot point within the story. These interestingness values are based on real human players’ interactions with the story. We also present a drama manager that uses a search process (based on the expectimax algorithm) and combines the author specified aesthetic values with the player model.
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Towards Player Preference Modeling for Drama Management in Interactive Stories
by Manu Sharma, Santi Ontañón, Christina Strong, Manish Mehta, Ashwin Ram
20th International FLAIRS Conference on Artificial Intelligence (FLAIRS-07), Key West, FL, May 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-03.pdf
11
Jan
Posted by cognitivecomputing in Game AI, Learning. Tagged: case-based reasoning, games, planning, real-time cbr, rts games. 2 comments
The goal of transfer learning is to use the knowledge acquired in a set of source tasks to improve performance in a related but previously unseen target task. In this paper, we present a multi-layered architecture named CAse-Based Reinforcement Learner (CARL). It uses a novel combination of Case-Based Reasoning (CBR) and Reinforcement Learning (RL) to achieve transfer while playing against the Game AI across a variety of scenarios in MadRTS, a commercial Real-Time Strategy game. Our experiments demonstrate that CARL not only performs well on individual tasks but also exhibits significant performance gains when allowed to transfer knowledge from previous tasks.
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Transfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL
by Manu Sharma, Michael Holmes, Juan Santamaria, Arya Irani, Charles Isbell, Ashwin Ram
International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India, January 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-01.pdf
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