Computer Science & Engineering
Document Content Inventory and Retrieval
We give an analysis of relationships between expected retrieval performance and classication recognition accuracy in the context of document image content extraction and inventory. By content extraction we mean location and measurement of regions containing handwriting, machine-printed text, photographs, blank space, etc, in documents represented as bilevel, grey-level, or color images. Recent experiments have shown that even modest per-pixel content classication accuracies can support usefully high recall and precision rates (of, e.g., 8090%) for retrieval queries within document collections seeking pages that contain a given minimum fraction of a certain type of content. In an effort to elucidate this interesting empirical result, we have analyzed the interdependency of classication and retrieval under a variety of assumptions about the distribution of content types in document image collections. We show that under general conditions we cannot derive precision and recall measures from per-pixel classication measures alone, but we can estimate the expected values of these measures. If however the distribution of content and error rates are uniform across the entire collection, our results suggest, it is possible to predict precision and recall measures from classication accuracy and vice versa. historical documents; rectilinear and complex non-rectilinear layouts; and clean and degraded images.
Game AI for a Turn-based Strategy Game with Plan Adaptation and Ontology-based Retrieval
In this paper we present a novel approach for developing adaptive game AI by combining case based planning techniques and ontological knowledge from the game environment. The proposed architecture combines several components: a case-based hierarchical planner (Repair-SHOP), a bridge to connect and reason with Ontologies formalized in Description Logics (DLs) based languages (OntoBridge), a DLs reasoner (Pellet) and a framework to develop Case-Based Reasoning (CBR) systems (jCOLIBRI ). In our ongoing work we are applying this approach to a commercial Civilization clone turn-based strategy game (CTP2) where game AI is in charge of planning the strategies for automated players. Our goal is to demonstrate that ontology-based retrieval will result in the retrieval of strategies that are easier to adapt than those plans returned by other classical retrieval mechanisms traditionally used in case-based planning.
Transfer Learning of Hierarchical Task-Network Planning Methods in a Real-Time Strategy Game
We describe a new integrated and automated AI planning and learning architecture, called Learn2SHOP. Learn2SHOP departs significantly from the previous works on AI planning and learning in that its modular architecture integrates Hierarchical Task Network (HTN) planning, concept learning, and computer simulations. Using simulations during the planning and learning process enables the system to get information about the outcomes of the actions. We have implemented Learn2SHOP and tested it on a transfer-learning task. The objective of transfer learning is transferring knowledge and skills learned from a wide variety of previous situations to the current, and likely different, previously unencountered problems(s). The experiments with Learn2SHOP have demonstrated the advantages of integrating planning, learning, and simulation in a real-time strategy game engine.
SHIM: A Scalable Hierarchical Inter-domain Multicast Approach for Disruption Tolerant Networks
Disruption Tolerant Network (DTN) technologies are emerging solutions to networks that experience frequent partitions. In this paper, we propose the scalable hierarchical inter-domain multicast (SHIM) approach for DTNs. SHIM has the following characteristics: i) it is capable of delivering multicast messages to receivers distributed in different domains; ii) the size of the membership information maintained by the source leader is determined by its out-degree in the leader layer, no matter how large the number of the real receivers is; and iii) it at least doubles the message delivery efficiency than that of directly extending the existing intra-domain DTN multicast methods to perform the inter-domain multicast operations. Our results also show that the message delivery ratio of SHIM can be improved to be almost 100% when the custodian transfer functionality is enabled in the overall networks.
RETALIATE: Learning Winning Policies in First-Person Shooter Games
In this paper we present RETALIATE, an online reinforcement learning algorithm for developing winning policies in team first-person shooter games. RETALIATE has three crucial characteristics: (1) individual BOT behavior is fixed although not known in advance, therefore individual BOTS work asplug-ins, (2) RETALIATE models the problem of learning team tactics through a simple state formulation, (3) discount rates commonly used in Q-learning are not used. As a result of these characteristics, the application of the Q-learning algorithm results in the rapid exploration towards a winning policy against an opponent team. In our empirical evaluation we demonstrate that RETALIATE adapts well when the environment changes.