Transposition tables are a well-known method for pruning duplicates in heuristic search. This paper presents a detailed analysis of transposition tables for IDA*. We show that some straightforward implementations of IDA* with transposition tables (IDA*+TT) can result in suboptimal solutions being returned. Furthermore, straightforward implementations of IDA*+TT are not complete. We identify several variants of IDA*+TT which are guaranteed to return the optimal solution, as well as a complete variant. An empirical study shows that IDA*+TT can significantly improve upon the performance of A* in domain-independent planning.

Ontologies [1] are used in computer science for representing and sharing knowledge about the real world. Usually ontological structures are described in terms of classes(of things) and relationships(between things). This is rather similar to category-theoretic notions of objects and morphisms (see [2, 3] for information about the algebraic category theory). Since the category theory already brings us many benefits in other areasofcomputer science, it is desirable to find arrowtheoretic approaches in the area of knowledge representation. 1 Some authors proposed category-theoretic techniques helpful in different aspects of knowledge representation[5, 6]. Usually they operate with (co)limits that are convenient for merging and interoperating between existing models and metamodels. Our aim is to find a category-theoretic tools that would be useful for description of ontological models from the very beginning.

Answering queries posed over knowledge bases is a central problem in knowledge representation and database theory. In the database area, checking query containment is an important query optimization and schema integration technique. In knowledge representation it has been used for object classification, schema integration, service discovery, and more. In the presence of a knowledge base, the problem of query containment is strictly related to that of query answering; indeed, the two are reducible to each other; we focus on the latter, and our results immediately extend to the former.

However, there are many domains, work that is perhaps closest to ours is the "dead-end heuristic" such as map-based searches (common in GPS navigation, introduced by Björnsson and Halldórsson (2006). They computer games, and robotics) where the entire use a preprocessing phase to identify areas that are deadends, state-space is given explicitly. Optimal paths for such domains and create an abstract graph whose nodes are these can be found relatively quickly with simple heuristics, areas. Initially, the search is performed on the abstracted especially when compared to the time it takes to explore graph. The areas that were not visited during the search exponentially large combinatorial problems. Relative on the abstracted graph are then ignored when the search is quickness, however, might still not be fast enough in certain performed in the original search space. In addition to identifying real-time applications, where further improvement towards dead-ends, our approach also identifies (and prunes, high-speed performance is especially valued.

In problem domains where an informative heuristic evaluation function is not known or not easily computed, abstraction can be used to derive admissible heuristic values. Optimal path lengths in the abstracted problem are consistent heuristic estimates for the original problem. Pattern databases are the traditional method of creating such heuristics, but they exhaustively compute costs for all abstract states and are thus usually appropriate only when all instances share the same single goal state. Hierarchical heuristic search algorithms address these shortcomings by searching for paths in the abstract space on an as-needed basis. However, existing hierarchical algorithms search less efficiently than pattern database constructors: abstract nodes may be expanded many times during the course of a base-level search. We present a novel hierarchical heuristic search algorithm, called Switchback, that uses an alternating direction of search to avoid abstract node re-expansions. This algorithm is simple to implement and demonstrates superior performance to existing hierarchical heuristic search algorithms on several standard benchmarks.

This paper analyzes the performance of IDA* using additive heuristics. We show that the reduction in the number of nodes expanded using multiple independent additive heuristics is the product of the reductions achieved by the individual heuristics. First, we formally state and prove this result on unit edge-cost undirected graphs with a uniform branching factor. Then, we empirically verify it on a model of the 4-peg Towers of Hanoi problem. We also run experiments on the multiple sequence alignment problem showing more general applicability to non-unit edge-cost directed graphs. Then, we extend an existing model to predict the performance of IDA* with a single pattern database to independent additive disjoint pattern databases. This is the first analysis of the performance of independent additive heuristics.

Based on a typology of five basic forms of abduction, I propose a new definition of abductive insight that empha sizes in particular the inferential structure of a belief system that is able to explain a phenomenon after a new, abductive ly created component has been added to this system or the entire system has been abductively restructured. My thesis is, first, that the argumentative structure of the pursued problem solution guides abductive creativity and, second, that diagrammatic reasoning—if conceptualized according to the requirements defined by Charles Peirce—can support this guidance. This support is mainly possible based on the normative power of the system of representation that has to be used to construct diagrams and to perform experiments with them.

Tactile maps offer access to spatial-analog information for visually impaired people. In contrast to visual maps, a tactile map has a lower resolution and can only be inspected in a sequential way, complicating the extraction of spatial relations among distant map entities. Verbal assistance can help to overcome these difficulties by substituting textual labels with verbal descriptions and offering propositional knowledge about spatial relations. Like visual maps, tactile maps are based on visual, spatial-geometric representations that need to be reasoned about in order to generate verbal assistance. We present an approach towards a verbally assisting virtual-environment tactile map (VAVETaM) realized on a computer system utilizing a haptic force-feedback device. In particular, we discuss the tasks of understanding the user's map exploration procedures (MEPs), of exploiting the spatial-analog map to anticipate the user's informational needs, of reasoning about optimal assistance by taking assumed prior knowledge of the user into account, and of generating appropriate verbal instructions and descriptions to augment the map.

A distributed non-hierarchical metacognitive architec- ture is one in which all meta-level reasoning compo- nents are subject to meta-level monitoring and manage- ment by other components. Such metacognitive distri- bution can support the robustness of distributed IT sys- tems in which humans and artificial agents are partic- ipants. However, robust metacognition also needs to be context-aware and use diversity in its reasoning and analysis methods. Both these requirements mean that an agent evaluates its reasoning within a “bigger picture” and that it can monitor this global picture from multi- ple perspectives. In particular, social context-awareness involves understanding the goals and concerns of users and organisations. In this paper, we first present a conceptual architecture for distributed metacognition with context-awareness and diversity. We then consider the challenges of apply- ing this architecture to autonomic management systems in scenarios where agents must collectively diagnose and respond to errors and intrusions. Such autonomic systems need rich semantic knowledge and diverse data sources in order to provide the necessary context for their metacognitive evaluations and decisions.

The starting point is an ongoing effort to structure underlying intelligent behavior, whether intended reconstruct cognitive architectures from the ground up via as models of human intelligence and/or implementations of graphical models (Koller and Friedman 2009), with the artificial intelligence (Langley, Laird and Rogers 2009). A aim of understanding existing architectures better, basic cognitive architecture may comprise memories, exploring the overall space of architectures, and decision algorithms, learning mechanisms, and some developing new and improved architectures (Rosenbloom means of interacting with external environments.