Tightest Admissible Shortest Path

The shortest path problem in graphs is fundamental to AI. Nearly all variants of the problem and relevant algorithms that solve them ignore edge-weight computation time and its common relation to weight uncertainty. This implies that taking these factors into consideration can potentially lead to a performance boost in relevant applications. Recently, a generalized framework for weighted directed graphs was suggested, where edge-weight can be computed (estimated) multiple times, at increasing accuracy and run-time expense. We build on this framework to introduce the problem of finding the tightest admissible shortest path (TASP); a path with the tightest suboptimality bound on the optimal cost. This is a generalization of the shortest path problem to bounded uncertainty, where edge-weight uncertainty can be traded for computational cost. We present a complete algorithm for solving TASP, with guarantees on solution quality. Empirical evaluation supports the effectiveness of this approach.Comment: arXiv admin note: text overlap with arXiv:2208.1148

Towards Social Comparison for Failure Detection

Abstract Social comparison, the process in which individuals compare their behavior and beliefs to those of other agents, is an important process in human societies. Our aim is to utilize theories of this process for synthetic agents, for the purposes of enabling social skills, teamcoordination, and greater individual agent performance. Our current focus is on individual failure detection and recovery in multi-agent settings. We present a novel approach, SOCFAD, inspired by Social Comparison Theory from social psychology. SOCFAD includes the following key novel concepts: (a) utilizing other agents the environment as information sources for failure detection, and (b) a detection and recovery method for previously undetectable failures using abductive inference based on other agents' beliefs 1

An efficient behavior classifier based on distributions of relevant events

Proceeding of: European Conference on Artificial Intelligence (ECAI 2008). Patras, Greece, july, 21st-25th, 2008.This work has been supported by the Spanish Ministry of Education and Science under project TRA-2007-67374-C02-02.Publicad

A plan classifier based on Chi-square distribution tests

To make good decisions in a social context, humans often need to recognize the plan underlying the behavior of others, and make predictions based on this recognition. This process, when carried out by software agents or robots, is known as plan recognition, or agent modeling. Most existing techniques for plan recognition assume the availability of carefully hand-crafted plan libraries, which encode the a-priori known behavioral repertoire of the observed agents; during run-time, plan recognition algorithms match the observed behavior of the agents against the plan-libraries, and matches are reported as hypotheses. Unfortunately, techniques for automatically acquiring plan-libraries from observations, e.g., by learning or data-mining, are only beginning to emerge. We present an approach for automatically creating the model of an agent behavior based on the observation and analysis of its atomic behaviors. In this approach, observations of an agent behavior are transformed into a sequence of atomic behaviors (events). This stream is analyzed in order to get the corresponding behavior model, represented by a distribution of relevant events. Once the model has been created, the proposed approach presents a method using a statistical test for classifying an observed behavior. Therefore, in this research, the problem of behavior classification is examined as a problem of learning to characterize the behavior of an agent in terms of sequences of atomic behaviors. The experiment results of this paper show that a system based on our approach can efficiently recognize different behaviors in different domains, in particular UNIX command-line data, and RoboCup soccer simulationThis work has been partially supported by the Spanish Government under project TRA2007-67374-C02-0

Classifying efficiently the behavior of a Soccer team

Proceeding of: 10th International Conference on Intelligent Autonomous Systems (IAS 2008), Baden Baden, Germany, July 23-25th, 2008.In order to make a good decision, humans usually try to predict the behavior of others. By this prediction, many different tasks can be performed, such as to coordinate with them, to assist them or to predict their future behavior. In competitive domains, to recognize the behavior of the opponent can be very advantageous. In this paper, an approach for creating automatically the model of the behavior of a soccer team is presented. This approach is an effective and notable improvement of a previous work. As the actions performed by a soccer team are sequential, this sequentiality should be considered in the modeling process. Therefore, the observations of a soccer team in a dynamic, complex and continuous multi-variate world state are transformed into a sequence of atomic behaviors. Then, this sequence is analyzed in order to find out a model that defines the team behavior. Finally, the classification of an observed team is done by using a statistical test.This work has been supported by the Spanish Ministry of Education and Science under project TRA-2007-67374-C02-02.Publicad

An overview of RoboCup-2002 Fukuoka/Busan

© 2003, American Association for Artificial Intelligence (AAAI). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/This article reports on the Sixth Robot World Cup Competition and Conference (RoboCup-2002) Fukuoka/Busan, which took place from 19 to 25 June in Fukuoka, Japan. It was the largest RoboCup since 1997 and held the first humanoid league competition in the world. Further, the first ROBOTREX (robot trade and exhibitions) was held with about 50 companies, universities, and institutes represented. A total of 117,000 spectators witnessed this marvelous event, To the best of our knowledge, this was the largest robotic event in history.Peer reviewe