Current methods of routing are based on network information in the form of routing tables, in which routing protocols determine how to update the tables according to the network changes. Despite the variability of data in routing tables, node addresses are constant. In this paper, we first introduce the new concept of variable addresses, which results in a novel framework to cope with routing problems using heuristic solutions. Then we propose a heuristic routing mechanism based on the application of genes for determination of network addresses in a variable address network and describe how this method flexibly solves different problems and induces new ideas in providing integral solutions for variety of problems. The case of ad-hoc networks is where simulation results are more supportive and original solutions have been proposed for issues like mobility.

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.

Sourcing professionals buy several trillion dollars worth of goods and services yearly. We introduced a new paradigm called expressive commerceand applied it to sourcing. It combines the advantages of highly expressive human negotiation with the advantages of electronic reverse auctions. The idea is that supply and demand are expressed in drastically greater detail than in traditional electronic auctions and are algorithmically cleared. This creates a Pareto efficiency improvement in the allocation (a win-win between the buyer and the sellers), but the market-clearing problem is a highly complex combinatorial optimization problem. We developed the world's fastest tree search algorithms for solving it. We have hosted $35 billion of sourcing using the technology and created $4.4 billion of hard-dollar savings plus numerous harder-to-quantify benefits. The suppliers also benefited by being able to express production efficiencies and creativity, and through exposure problem removal. Supply networks were redesigned, with quantitative understanding of the trade-offs, and implemented in weeks instead of months.

Once the rules are formulated, This knowledge base is developed and maintained how does the stimuli generator ensure by knowledge engineers who are verification that all user-defined and validity rules, and as experts. Test templates are written by many expert knowledge rules as possible, are verification engineers who implement the test satisfied? How can the generator produce many significantly different tests from the plan. The generic engine, developed by software same test template? Finally, how is all this done engineers, accepts the architecture model, in an efficient manner as to not obstruct the expert knowledge, and test template and generates verification process?

(ISAIM 2008).

The Internet contains a very large number of information sources providing many types of data from weather forecasts to travel deals and financial information. These sources can be accessed via Web-forms, Web Services, RSS feeds and so on. In order to make automated use of these sources, we need to model them semantically, but writing semantic descriptions for Web Services is both tedious and error prone. In this paper we investigate the problem of automatically generating such models. We introduce a framework for learning Datalog definitions of Web sources. In order to learn these definitions, our system actively invokes the sources and compares the data they produce with that of known sources of information. It then performs an inductive logic search through the space of plausible source definitions in order to learn the best possible semantic model for each new source. In this paper we perform an empirical evaluation of the system using real-world Web sources. The evaluation demonstrates the effectiveness of the approach, showing that we can automatically learn complex models for real sources in reasonable time. We also compare our system with a complex schema matching system, showing that our approach can handle the kinds of problems tackled by the latter.

I postulate that human or other intelligent agents function or should function as follows. They store all sensory observations as they come - the data is holy. At any time, given some agent's current coding capabilities, part of the data is compressible by a short and hopefully fast program / description / explanation / world model. In the agent's subjective eyes, such data is more regular and more "beautiful" than other data. It is well-known that knowledge of regularity and repeatability may improve the agent's ability to plan actions leading to external rewards. In absence of such rewards, however, known beauty is boring. Then "interestingness" becomes the first derivative of subjective beauty: as the learning agent improves its compression algorithm, formerly apparently random data parts become subjectively more regular and beautiful. Such progress in compressibility is measured and maximized by the curiosity drive: create action sequences that extend the observation history and yield previously unknown / unpredictable but quickly learnable algorithmic regularity. We discuss how all of the above can be naturally implemented on computers, through an extension of passive unsupervised learning to the case of active data selection: we reward a general reinforcement learner (with access to the adaptive compressor) for actions that improve the subjective compressibility of the growing data. An unusually large breakthrough in compressibility deserves the name "discovery". The "creativity" of artists, dancers, musicians, pure mathematicians can be viewed as a by-product of this principle. Several qualitative examples support this hypothesis.

When Kurt Goedel layed the foundations of theoretical computer science in 1931, he also introduced essential concepts of the theory of Artificial Intelligence (AI). Although much of subsequent AI research has focused on heuristics, which still play a major role in many practical AI applications, in the new millennium AI theory has finally become a full-fledged formal science, with important optimality results for embodied agents living in unknown environments, obtained through a combination of theory a la Goedel and probability theory. Here we look back at important milestones of AI history, mention essential recent results, and speculate about what we may expect from the next 25 years, emphasizing the significance of the ongoing dramatic hardware speedups, and discussing Goedel-inspired, self-referential, self-improving universal problem solvers.

In this paper, we employ Probabilistic Neural Network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition algorithm. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.

This research hypothesized that a practical approach in the form of a solution framework known as Natural Language Understanding and Reasoning for Intelligence (NaLURI), which combines full-discourse natural language understanding, powerful representation formalism capable of exploiting ontological information and reasoning approach with advanced features, will solve the following problems without compromising practicality factors: 1) restriction on the nature of question and response, and 2) limitation to scale across domains and to real-life natural language text.