We propose a novel deep web crawling framework based on reinforcement learning. The crawler is regarded as an agent and deep web database as the environment. The agent perceives its current state and submits a selected action (query) to the environment according to Q-value. Based on the framework we develop an adaptive crawling method. Experimental results show that it outperforms the state of art methods in crawling capability and breaks through the assumption of full-text search implied by existing methods.

To function in human-inhabited environments a robot must be able to press buttons. There are literally thousands of different buttons, which produce various types of feedback when pressed. This work focuses on doorbell buttons, which provide auditory feedback. Our robot learned to predict if a specific pushing movement would press a doorbell button and produce a sound. The robot explored different buttons with random pushing behaviors and perceived the proprioceptive, tactile, and acoustic outcomes of these behaviors.

Evolutionary trees of species can be reconstructed by pairwise comparison of their entire genomes. Such a comparison can be quantified by determining the number of events that change the order of genes in a genome. Earlier Erdem and Tillier formulated the pairwise comparison of entire genomes as the problem of planning rearrangement events that transform one genome to the other. We reformulate this problem as a planning problem to extend its applicability to genomes with multiple copies of genes and with unequal gene content, and illustrate its applicability and effectiveness on three real datasets: mitochondrial genomes of Metazoa, chloroplast genomes of Campanulaceae, chloroplast genomes of various land plants and green algae.

In this paper a system was developed for robot behavior acquisition using kinesthetic demonstrations. It enables a humanoid robot to imitate constrained reaching gestures directed towards a target using a learning algorithm based on Gaussian Mixture Models. The imitation trajectory can be reshaped in order to satisfy the constraints of the task and it can adapt to changes in the initial conditions and to target displacements occurring during movement execution. The potential of this method was evaluated using experiments with the Nao, Aldebaran’s humanoid robot.

In this abstract, we compare semantic search (in the RDF model) with keyword search (in the relational model), and illustrate how these two search paradigms are different. This comparison addresses the following questions: (1) What can semantic search achieve that keyword search can not (in terms of behavior)? (2) Why is it difficult to simulate semantic search, using keyword search on the relational data model? We use the term keyword search, when the search is performed on data stored in the relational data model, as in traditional relational databases, and an example of keyword search in databases is [Hri02]. We use the term semantic search, when the search is performed on data stored in the RDF data model. Note that when the data is modeled in RDF, it inherently contains explicit typed relations or semantics, and hence the use of the term “semantic search.” Let us begin with an example, to illustrate the differences between semantic search and keyword search.

Forming teams using heterogeneous agents that perform well together to accomplish a task in a game can be a challenging problem. There can often be an enormous amount of combinations to look through, and having an agent that is really good at a particular task is no guarantee that agent will perform well on a team with members with different abilities. Picking a good team is important, as changing teams is often not allowed midway through a task.

The reinforcement learning (RL) paradigm typically assumes a class of efficient, general search procedures that search a given reward function that is part of the problem over the space of programs--to search for reward functions. However, in animals, all reward These reward functions operate over the entire state space of signals are generated internally, rather than being received a reinforcement learning problem and, if successful, will be directly from the environment. Furthermore, animals able to quickly and automatically identify relevant variables have evolved motivational systems that facilitate learning by and features of the problem. This will allow the agent to rewarding activities that often bear a distal relationship to outperform an agent that uses the obvious task-based reward the animal's ultimate goals. Such intrinsic motivation can function. The use of genetic programming methods may alleviate cause an agent to explore and learn in the absence of external the difficulty of scaling reward function search and rewards, possibly improving its performance over a set provide a natural way to search through a very expressive of problems.

The field of multi-robot coordination, specifically robot formation control, is rapidly expanding, with many applications proposed. In our previous work, we considered the problem of establishing and maintaining a formation of robots given an already connected network. We now propose a distributed auction-based method to autonomously initialize and reorganize the network structure of a formation of robots.

Document clustering has been applied in web information retrieval, which facilitates users’ quick browsing by organizing retrieved results into different groups. Meanwhile, a tree-like hierarchical structure is wellsuited for organizing the retrieved results in favor of web users. In this regard, we introduce a new method for hierarchical clustering of web snippets by exploiting a phrase-based document index. In our method, a hierarchy of web snippets is built based on phrases instead of all snippets, and the snippets are then assigned to the corresponding clusters consisting of phrases. We show that, as opposed to the traditional hierarchical clustering, our method not only presents meaningful cluster labels but also improves clustering performance.

Contrary to the previous beliefs that all arrived streaming data are labeled and the class labels are immediately availa- ble, we propose a Semi-supervised classification algorithm for data streams with concept drifts and UNlabeled data, called SUN. SUN is based on an evolved decision tree. In terms of deviation between history concept clusters and new ones generated by a developed clustering algorithm of k-Modes, concept drifts are distinguished from noise at leaves. Extensive studies on both synthetic and real data demonstrate that SUN performs well compared to several known online algorithms on unlabeled data. A conclusion is hence drawn that a feasible reference framework is provided for tackling concept drifting data streams with unlabeled data.