If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This work presents an algorithm to provide a better represen- tation of space to artificially intelligent characters (i.e., agents or bots) in game and simulation environments by providing a more accurate breakdown of the traversable space present in the game environment. Such representations are generally constructed by decomposing the walkable space present in a game environment into a series of convex regions to form a data structure called a navigation mesh. We extend the basic concept of a navigation mesh by the introduction of an understanding of the textures that are attached to the underlying geometry creating what we refer to as a texture-aware navigation mesh. This does result in a more complex navigation mesh (more regions and a larger search space). However, since the textures of walkable geometry can be used to determine the appropriate traversal method for that terrain, a game character can determine valid paths for their traversal methods using just the navigation mesh (e.g., characters in cars can generate paths containing just roads or walking characters can create paths containing just sidewalks). We also present a use case that shows how such a system of texture aware naviga- tion meshes might benefit character path planning and search in virtual environments. In this use case, we examine a Real Time Strategy game style game environment, which shows it is possible to generate a navigation mesh such that each region is composed of a single terrain type.
Eichelberger, Christopher (The University of North Carolina at Charlotte) | Hadzikadic, Mirsad (The University of North Carolina at Charlotte) | Gajic, Ognjen (The Mayo Clinic) | Li, Guangxi (The Mayo Clinic)
Complex adaptive systems (CAS) promise to be useful in modeling and understanding real-world phenomena, but remain difficult to validate and verify. The authors present an adaptive, tool-chain-based approach to continuous validation and verification that allows the subject matter experts (SMEs) and modelers to interact in a useful manner. A CAS simulation of the ICU at the Mayo Clinic is used as a working example to illustrate the method and its benefits.
To support more precise query translation for English-Chinese Bi-Directional Cross-Language Information Retrieval (CLIR), we have developed a novel framework by integrating a semantic network to characterize the correlations between multiple inter-related text terms of interest and learn their inter-related statistical query translation models. First, a semantic network is automatically generated from large-scale English-Chinese bilingual parallel corpora to characterize the correlations between a large number of text terms of interest. Second, the semantic network is exploited to learn the statistical query translation models for such text terms of interest. Finally, these inter-related query translation models are used to translate the queries more precisely and achieve more effective CLIR. Our experiments on a large number of official public data have obtained very positive results.
Youngblood, G. Michael (The University of North Carolina at Charlotte) | Heckel, Frederick W. P. (The University of North Carolina at Charlotte) | Hale, D. Hunter (The University of North Carolina at Charlotte) | Carroll, Arthur (The University of North Carolina at Charlotte)
First/third-person simulations in virtual environments have become increasingly used in training; however, creating intelligent, interactive characters to populate these environments presents a large authorial burden. Our work focuses on building tools to enable rapid creation of intelligent characters for first/third-person game-like environments with no programming knowledge required by the user. This is made possible using behavior-based control combined with a user interface employing natural language-like character specification in the form of English sentences and interactive testing during development.
Embedded systems consisting of collaborating agents capable of interacting with their environment are becoming ubiquitous. It is crucial for these systems to be able to adapt to the dynamic and uncertain characteristics of an open environment. In this paper, we argue that multiagent meta-level control (MMLC) is an effective way to determine when this adaptation process should be done and how much effort should be invested in adaptation as opposed to continuing with the current action plan. We describe a reinforcement learning based approach to learn decentralized meta-control policies offline. We then propose to use the learned reward model as input to a global optimization algorithm to avoid conflicting meta-level decisions between coordinating agents. Our initial experiments in the context of NetRads, a multiagent tornado tracking application show that MMLC significantly improves performance in a 3-agent network.
It is crucial for social systems to adapt to the dynamics of open environments. This adaptation process becomes especially challenging in the context of multiagent systems. In this paper, we argue that multiagent meta-level control is an effective way to determine when this adaptation process should be done and how much effort should be invested in adaptation as opposed to continuing with the current action plan. We develop a reinforcement learning based mechanism for multiagent meta-level control that facilitates the metalevel control component of each agent to learn policies in a decentralized fashion that (a) it can efficiently support agent interactions with other agents and (b) reorganize the underlying network when needed. We evaluate this mechanism in the context of a multiagent tornado tracking application called NetRads. Empirical results show that adaptive multiagent meta-level control significantly improves the performance of the tornado tracking network for a variety of weather scenarios.
Heckel, Frederick W. P. (The University of North Carolina at Charlotte) | Youngblood, G. Michael (The University of North Carolina at Charlotte) | Hale, D. Hunter (The University of North Carolina at Charlotte)
With the most resource intensive tasks in games offloaded to special purpose processors, game designers now have the opportunity to build richer characters using more complex AI techniques than have been used in the past. While additional CPU time makes improved AI feasible, better tools for building agents are needed to make good interactive characters a reality. In this paper we present the BEHAVEngine and BehaviorShop which enable the creation of rich interactive characters.
We present a modified navigation mesh generation algorithm that allows the mesh to be dynamically altered at runtime. We accomplish this using an extension to the existing spatial decomposition algorithm ASFV (Adaptive Space Filling Volumes) that will allow the algorithm to dynamically adapt to changes to the underlying world geometry without having to rebuild the entire spatial decomposition. This is accomplished by providing two algorithms to deal with alterations to the world. The ability is provided to add arbitrary obstructions into what was negative space and then to build a new correct spatial decomposition around the new obstruction. Functionality is also provided to remove existing obstructions and then to build up new decompositions to fill in the newly created negative space. Finally, we show via an experiment that our dynamic extensions to ASFV reduces the cost of correcting an invalidated decomposition by 90% or more.