By Yves Demazeau, Franco Zambonelli, Juan M. Corchado, Javier Bajo (eds.)
This publication constitutes the refereed lawsuits of the twelfth foreign convention on sensible purposes of brokers and Multi-Agent structures, PAAMS 2014, held in Salamanca, Spain, in June 2014. The 12 revised complete papers and 14 brief papers have been rigorously reviewed and chosen from fifty two submissions and are provided including 19 demonstrations. The papers record at the program and validation of agent-based types, tools, and applied sciences in a few key program parts, together with: agent-oriented software program engineering, conversations, movement coordination and unmanned aerial automobiles, internet and repair structures, robotics exploration, shrewdpermanent towns and infrastructures, and social systems.
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Additional info for Advances in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection: 12th International Conference, PAAMS 2014, Salamanca, Spain, June 4-6, 2014. Proceedings
Moreover,  proposed a taxonomy of dialogue acts (DIT++) based on the dialogue interpretation theory. Semantics of these dialogue acts are based on the IS based approach. We are motivated to use it to understand and interpret conversation between human-agent team due to its following characteristics: (i) it is mainly used for annotation and interpretation of dialogues in human-human conversation; (ii) it supports task oriented conversation; and (iii) it has become the ISO 24617-2 international standard for dialogue interpretation using dialogue acts.
2. Examples of hypothesis tree: a) one tree set, b) two tree set The hypothesis tree is updated in two cases: ─ Adding of hypothesis. A new hypothesis, that exceeds the hypothesis acceptance threshold, is proposed during the map merging. A hypothesis acceptance threshold is exceeded, if the similarity of maps computed by some similarity metric is higher than the preset threshold value. Usually this threshold represents the relative number of cells that must be similar in the common part of maps .
The map similarity value is computed using Equation 2. The weight of ‘occupied’ cells varies from 0 to 1. 5, the influence of ‘occupied’ cell similarity impacts the result more than ‘free’ cells, and vice versa. MSm1,m2 = wocc * socc + (1 – wocc) * sfree (2) wocc – ‘occupied’ cell weight, socc – ‘occupied’ cell similarity evaluation, sfree – ‘free cell similarity evaluation. To compute the similarity only one parameter is required – the distance threshold dmax that describes, how large the mapping error is allowed to be in the given mapping system.