Auction mechanisms are very well known methods to allocate tasks when several agents are involved. Particularly, multi-attribute auctions are a special mechanism that allows the consideration of task attributes other than prices, such as delivery time or energy consumptions. Incentive compatible mechanisms encourage agents to reveal the attributes which agents estimate truthful, however, these mechanisms by themselves cannot know if such estimations are reliable or not due to uncertainty. Under such circumstances, trust could complement incentive compatibility reducing the risk of losses by the auctioneer. The use of trust in auctions is a well-studied problem; however, most of the works in the literature focus on how to model trust rather on how trust is used in the mechanism. Thus, this paper proposes an easy and systematic way to include a multi-faceted model of trust into multi-attribute auctions. Conversely to other previous works where trust is only used in the winner determination problem, the presented approach uses trust both in deciding the winner of the auction and in the payment to the corresponding bidder. According to the results obtained from the experimentation, the use of trust following the methodology presented in this paper highly reduces the number of winner bids from unreliable bidders and, therefore, the number of tasks executed in worse conditions than the agreed. Complementary, this paper proposes a new trust adaptation method which consists of increasing or decreasing the trust value (depending on whether the task is executed properly or not) according to a simple mathematical function with asymptotes on 0 and 1. This model does not present the rigidity problem present in other models of the literature when it comes to agents that have inconstant performances.

We propose a mechanism to learn communicative action structure to analyze adversarial reasoning patterns in customer complaints. An efficient way to assist customers and companies is to reuse previous experience with similar agents. A formal representation of customer complaints and a machine learning technique for handling scenarios of interaction between conflicting human agents are proposed. It is shown that analyzing the structure of communicative actions without context information is frequently sufficient to advise on complaint resolution strategies. Therefore, being domain-independent, the proposed machine learning technique is a good complement to a wide range of customer response management applications where formal treatment of inter-human interactions is required.

We build a mechanism to form an ontology of entities which improves a relevance of matching and searching microtext. Ontology construction starts from the seed entities and mines the web for new entities associated with them. To form these new entities, machine learning of syntactic parse trees (syntactic generalization) is applied to form commonalities between various search results for existing entities on the web. Ontology and syntactic generalization are applied to relevance improvement in search and text similarity assessment in commercial setting; evaluation results show substantial contribution of both sources to microtext processing.

We introduce a search engine and information retrieval system for providing access to opinion data. Natural language technology of generalization of syntactic parse trees is introduced as a similarity measure between subjects of textual opinions to link them on the fly. Information extraction algorithm for automatic summarization of web pages in the format of Google sponsored links is presented. We outline the usability of the implemented system, integrated opinion delivery environment (IODE).

The paper proposes a method that uses topological information to guide path planning in any 2D workspace. Our method builds a topological environment based on the workspace to compute homotopy classes, which topologically describe how paths go through the obstacles in the workspace. Then, the homotopy classes are sorted according to an heuristic estimation of their lower bound. Only those with smaller lower bound are used to guide a planner based on the Rapidly-exploring Random Tree (RRT), called Homotopic RRT (HRRT), to compute the path in the workspace. Simulated and real results with an Autonomous Underwater V ehicle (AUV) are presented showing the feasibility of the proposal. Comparison with well-known path planning algorithms has also been included.

We select Google enable people to share structured data on the Web. Design sponsored link format as a basis for opinion sharing. To of web portals leverages the fact that value and usefulness encourage both business owner / advertiser and user to of data increases, when the degree of interlinks with other express their opinion in this form, we need a hybrid of data rises. It is especially true for opinion data, where trust information extraction and summarization techniques to to an aggregated opinion can be developed by a extract expressions suitable to form advertisement line demonstration of a highly interlinked sources of data of from a business web page.