scholarly journals Dynamic Control in Real-Time Heuristic Search

2008 ◽  
Vol 32 ◽  
pp. 419-452 ◽  
Author(s):  
V. Bulitko ◽  
M. Lustrek ◽  
J. Schaeffer ◽  
Y. Bjornsson ◽  
S. Sigmundarson
Keyword(s):  
Real Time ◽  
Computer Games ◽  
Heuristic Search ◽  
Dynamic Control ◽  
Computer Game ◽  
Action Planning ◽  
Search Algorithms ◽  
Problem Size ◽  
Planning Time ◽  
Large Computer

Real-time heuristic search is a challenging type of agent-centered search because the agent's planning time per action is bounded by a constant independent of problem size. A common problem that imposes such restrictions is pathfinding in modern computer games where a large number of units must plan their paths simultaneously over large maps. Common search algorithms (e.g., A*, IDA*, D*, ARA*, AD*) are inherently not real-time and may lose completeness when a constant bound is imposed on per-action planning time. Real-time search algorithms retain completeness but frequently produce unacceptably suboptimal solutions. In this paper, we extend classic and modern real-time search algorithms with an automated mechanism for dynamic depth and subgoal selection. The new algorithms remain real-time and complete. On large computer game maps, they find paths within 7% of optimal while on average expanding roughly a single state per action. This is nearly a three-fold improvement in suboptimality over the existing state-of-the-art algorithms and, at the same time, a 15-fold improvement in the amount of planning per action.

scholarly journals Graph Abstraction in Real-time Heuristic Search

2007 ◽  
Vol 30 ◽  
pp. 51-100 ◽  
Author(s):  
V. Bulitko ◽  
N. Sturtevant ◽  
J. Lu ◽  
T. Yau
Keyword(s):  
Real Time ◽  
Heuristic Search ◽  
Search Space ◽  
Problem Size ◽  
Heuristic Function ◽  
Planning Time ◽  
Learning Speed ◽  
Heuristic Search Methods ◽  
State Abstraction ◽  
The Impact

Real-time heuristic search methods are used by situated agents in applications that require the amount of planning per move to be independent of the problem size. Such agents plan only a few actions at a time in a local search space and avoid getting trapped in local minima by improving their heuristic function over time. We extend a wide class of real-time search algorithms with automatically-built state abstraction and prove completeness and convergence of the resulting family of algorithms. We then analyze the impact of abstraction in an extensive empirical study in real-time pathfinding. Abstraction is found to improve efficiency by providing better trading offs between planning time, learning speed and other negatively correlated performance measures.

scholarly journals Case-Based Subgoaling in Real-Time Heuristic Search for Video Game Pathfinding

2010 ◽  
Vol 39 ◽  
pp. 269-300 ◽  
Author(s):  
V. Bulitko ◽  
Y. Björnsson ◽  
R. Lawrence
Keyword(s):  
Video Games ◽  
Real Time ◽  
Video Game ◽  
Heuristic Search ◽  
Scale Up ◽  
Computation Time ◽  
Search Algorithms ◽  
Problem Size ◽  
Solution Quality ◽  
Heuristic Search Algorithms

Real-time heuristic search algorithms satisfy a constant bound on the amount of planning per action, independent of problem size. As a result, they scale up well as problems become larger. This property would make them well suited for video games where Artificial Intelligence controlled agents must react quickly to user commands and to other agents' actions. On the downside, real-time search algorithms employ learning methods that frequently lead to poor solution quality and cause the agent to appear irrational by re-visiting the same problem states repeatedly. The situation changed recently with a new algorithm, D LRTA*, which attempted to eliminate learning by automatically selecting subgoals. D LRTA* is well poised for video games, except it has a complex and memory-demanding pre-computation phase during which it builds a database of subgoals. In this paper, we propose a simpler and more memory-efficient way of pre-computing subgoals thereby eliminating the main obstacle to applying state-of-the-art real-time search methods in video games. The new algorithm solves a number of randomly chosen problems off-line, compresses the solutions into a series of subgoals and stores them in a database. When presented with a novel problem on-line, it queries the database for the most similar previously solved case and uses its subgoals to solve the problem. In the domain of pathfinding on four large video game maps, the new algorithm delivers solutions eight times better while using 57 times less memory and requiring 14% less pre-computation time.

MODELING AND AUGMENTING GAME ENTERTAINMENT THROUGH CHALLENGE AND CURIOSITY

2007 ◽  
Vol 16 (06) ◽  
pp. 981-999 ◽  
Author(s):  
GEORGIOS N. YANNAKAKIS ◽  
JOHN HALLAM
Keyword(s):  
Real Time ◽  
Computer Games ◽  
Mixed Reality ◽  
Computer Game ◽  
Feedforward Neural Networks ◽  
Interactive Systems ◽  
Quantitative Measurements ◽  
Player Satisfaction ◽  
On Line ◽  
On Line Learning

This paper presents quantitative measurements/metrics of qualitative entertainment features within computer game environments and proposes artificial intelligence (AI) techniques for optimizing entertainment in such interactive systems. A human-verified metric of interest (i.e. player entertainment in real-time) for predator/prey games and a neuro-evolution on-line learning (i.e. during play) approach have already been reported in the literature to serve this purpose. In this paper, an alternative quantitative approach to entertainment modeling based on psychological studies in the field of computer games is introduced and a comparative study of the two approaches is presented. Feedforward neural networks (NNs) and fuzzy-NNs are used to model player satisfaction (interest) in real-time and investigate quantitatively how the qualitative factors of challenge and curiosity contribute to human entertainment. We demonstrate that appropriate non-extreme levels of challenge and curiosity generate high values of entertainment and we project the extensibility of the approach to other genres of digital entertainment (e.g. mixed-reality interactive playgrounds).

scholarly journals Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search

2020 ◽  
Vol 34 (06) ◽  
pp. 9827-9834
Author(s):  
Maximilian Fickert ◽  
Tianyi Gu ◽  
Leonhard Staut ◽  
Wheeler Ruml ◽  
Joerg Hoffmann ◽  
...  
Keyword(s):  
Real Time ◽  
Heuristic Search ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Experimental Results ◽  
Data Driven ◽  
Heuristic Search Algorithms ◽  
Original Presentation ◽  
Performance Statistics ◽  
The Cost

Suboptimal heuristic search algorithms can benefit from reasoning about heuristic error, especially in a real-time setting where there is not enough time to search all the way to a goal. However, current reasoning methods implicitly or explicitly incorporate assumptions about the cost-to-go function. We consider a recent real-time search algorithm, called Nancy, that manipulates explicit beliefs about the cost-to-go. The original presentation of Nancy assumed that these beliefs are Gaussian, with parameters following a certain form. In this paper, we explore how to replace these assumptions with actual data. We develop a data-driven variant of Nancy, DDNancy, that bases its beliefs on heuristic performance statistics from the same domain. We extend Nancy and DDNancy with the notion of persistence and prove their completeness. Experimental results show that DDNancy can perform well in domains in which the original assumption-based Nancy performs poorly.

scholarly journals Avoiding and Escaping Depressions in Real-Time Heuristic Search

2012 ◽  
Vol 43 ◽  
pp. 523-570 ◽  
Author(s):  
C. Hernandez ◽  
J. A. Baier
Keyword(s):  
Real Time ◽  
Heuristic Search ◽  
Search Algorithm ◽  
Search Space ◽  
Search Algorithms ◽  
Search Spaces ◽  
Heuristic Search Algorithms ◽  
Order Of Magnitude ◽  
Improved Performance ◽  
Hard Real Time

Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is inaccurate compared to the actual cost to reach a solution. Early real-time search algorithms, like LRTA*, easily become trapped in those regions since the heuristic values of their states may need to be updated multiple times, which results in costly solutions. State-of-the-art real-time search algorithms, like LSS-LRTA* or LRTA*(k), improve LRTA*'s mechanism to update the heuristic, resulting in improved performance. Those algorithms, however, do not guide search towards avoiding depressed regions. This paper presents depression avoidance, a simple real-time search principle to guide search towards avoiding states that have been marked as part of a heuristic depression. We propose two ways in which depression avoidance can be implemented: mark-and-avoid and move-to-border. We implement these strategies on top of LSS-LRTA* and RTAA*, producing 4 new real-time heuristic search algorithms: aLSS-LRTA*, daLSS-LRTA*, aRTAA*, and daRTAA*. When the objective is to find a single solution by running the real-time search algorithm once, we show that daLSS-LRTA* and daRTAA* outperform their predecessors sometimes by one order of magnitude. Of the four new algorithms, daRTAA* produces the best solutions given a fixed deadline on the average time allowed per planning episode. We prove all our algorithms have good theoretical properties: in finite search spaces, they find a solution if one exists, and converge to an optimal after a number of trials.

scholarly journals Personalized Tracking of Physical Activity in Children Using a Wearable Heart Rate Monitor

2020 ◽  
Vol 17 (16) ◽  
pp. 5895
Author(s):  
Santiago A. Pérez ◽  
Ana M. Díaz ◽  
Diego M. López
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Real Time ◽  
Computer Games ◽  
Serious Games ◽  
Significant Risk ◽  
Computer Game ◽  
Significant Risk Factor ◽  
Interactive System ◽  
User Characteristics

Serious games are video games that are intended to support learning while entertaining. They are considered valuable tools to improve user-specific skills or facilitate educational or therapeutic processes, especially in children. One of the disadvantages of computer games, in general, is their promotion of sedentary habits, considered as a significant risk factor for developing diseases such as obesity and hypertension. Exergames are serious games created to overcome the disadvantages of traditional computer games by promoting physical activity while playing. This study describes the development and evaluation of an adaptive component to monitor physical activity in children while using an exergame. The system is based on wearable technology to measure heart rate and perform real-time customizations in the exergame. To evaluate the adaptive component, an experiment was conducted with 30 children between 5 and 7 years of age, where the adaptive system was contrasted with a conventional interactive system (an exergame without adaptive component). It was demonstrated that the computer game, using the adaptive component, was able to change in real-time some of its functionalities based on the user characteristics. Increased levels of heart rate and caloric expenditure were significant in some of the game scenarios using the adaptive component. Although a formal user experience evaluation was not performed, excellent game playability and adherence by users were observed.

scholarly journals Approaching Artificial Intelligence for Games – the Turing Test revisited

1970 ◽  
Vol 4 (2) ◽  
pp. 167-171
Author(s):  
Jenny Eriksson Lundström ◽  
Stefan Karlsson
Keyword(s):  
Artificial Intelligence ◽  
Empirical Study ◽  
Real Time ◽  
Computer Games ◽  
Computer Game ◽  
Turing Test ◽  
Computer Applications ◽  
Specific Context ◽  
Computer Controlled

Today's powerful computers have increasingly more resources available, which can be used for incorporating more sophisticated AI into home applications like computer games. The perhaps obvious way of using AI to enhance the experience of a game is to make the player perceive the computer-controlled entities as intelligent. The traditional idea of how to determine whether a machine can pass as intelligent is the Turing Test. In this paper we show that it is possible and useful to conduct a test adhering to the intention of the original Turing test. We present an empirical study exploring human discrimination of artificial intelligence from the behaviour of a computer-controlled entity used in its specific context and how the behaviour responds to the user's expectations. In our empirical study the context is a real-time strategy computer game and the purpose of the AI is merely to pass as an acceptable opponent. We discuss the results of the empirical study and its implications for AI in computer applications.

scholarly journals Approaching Artificial Intelligence for Games – the Turing Test revisited

1970 ◽  
Vol 4 (2) ◽  
pp. 167-171
Author(s):  
Jenny Eriksson Lundström ◽  
Stefan Karlsson
Keyword(s):  
Artificial Intelligence ◽  
Empirical Study ◽  
Real Time ◽  
Computer Games ◽  
Computer Game ◽  
Turing Test ◽  
Computer Applications ◽  
Specific Context ◽  
Computer Controlled

Today's powerful computers have increasingly more resources available, which can be used for incorporating more sophisticated AI into home applications like computer games. The perhaps obvious way of using AI to enhance the experience of a game is to make the player perceive the computer-controlled entities as intelligent. The traditional idea of how to determine whether a machine can pass as intelligent is the Turing Test. In this paper we show that it is possible and useful to conduct a test adhering to the intention of the original Turing test. We present an empirical study exploring human discrimination of artificial intelligence from the behaviour of a computer-controlled entity used in its specific context and how the behaviour responds to the user's expectations. In our empirical study the context is a real-time strategy computer game and the purpose of the AI is merely to pass as an acceptable opponent. We discuss the results of the empirical study and its implications for AI in computer applications.

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