An automatic approach to extract goal plans from soccer simulated matches

Almeida, Fernando; Abreu, Pedro Henriques; Lau, Nuno; Reis, Luís Paulo

doi:10.1007/s00500-012-0952-z

Cited by 18 publications

(23 citation statements)

References 24 publications

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“…To validate the developed dialects, a group of experts and practitioners was formed. Most members of this group were informally consulted during the development stage of the dialects, using an iterative process by which each of them was able to both review the current state of the dialects and provide additional input for their development, in a Delphi-like process, used in many works (Abreu et al, 2012; Almeida et al, 2013). In addition, in a second stage, after the dialects were defined, a survey was conducted among the members, in order to assess their satisfaction with the quality of the dialects, based on the concepts and concerns each one brought forward.…”

Section: Validation and Resultsmentioning

confidence: 99%

Development of flexible languages for scenario and team description in multirobot missions

Silva

Abreu

Reis

et al. 2016

AIEDAM

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The work described in this paper is part of the development of a framework to support the joint execution of cooperative missions by a group of vehicles, in a simulated, augmented, or real environment. Such a framework brings forward the need for formal languages in which to specify the vehicles that compose a team, the scenario in which they will operate, and the mission to be performed. This paper introduces the Scenario Description Language (SDL) and the Team Description Language (TDL), two Extensible Markup Language based dialects that compose the static components necessary for representing scenario and mission knowledge. SDL provides a specification of physical scenario and global operational constraints, while TDL defines the team of vehicles, as well as team-specific operational restrictions. The dialects were defined using Extensible Markup Language schemas, with all required information being integrated in the definitions. An interface was developed and incorporated into the framework, allowing for the creation and edition of SDL and TDL files. Once the information is specified, it can be used in the framework, thus facilitating environment and team specification and deployment. A survey answered by practitioners and researchers shows that the satisfaction with SDL+TDL is elevated (the overall evaluation of SDL+TDL achieved a score of 4 out of 5, with 81%/78.6% of the answers ≥4); in addition, the usability of the interface was evaluated, achieving a score of 86.7 in the System Usability Scale survey. These results imply that SDL+TDL is flexible enough to represent scenarios and teams, through a user-friendly interface.

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Section: Validation and Resultsmentioning

confidence: 99%

Development of flexible languages for scenario and team description in multirobot missions

Silva

Abreu

Reis

et al. 2016

AIEDAM

Self Cite

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“…The most notable of these efforts are presented in [12]- [13]. For analysis of team performance F.Almeida et al [14] proposes a set play based approach for automatic goal plans extractionin the context of RoboCup Soccer Simulation 2D. In the domain of RoboCup Small Size League opponent strategy identification has also been explored by C.Erdogan and M.Veloso [15].…”

Section: Related Workmentioning

confidence: 99%

Opponent Classification in Robot Soccer

Larik

Haider

2015

Current Approaches in Applied Artificial Intelligence

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Abstract. The paper presents an approach to perform post-hoc analysis of RoboCup Soccer Simulation 3D teams via log files of their matches and to learn a model to classify them not only as being strong, medium or weak but also through their game playing styles such as frequent kickers, frequent dribblers, heavy/lean attackers, etc. The learned model can then be used to further cluster teams to predict game style of similar opponents. We have applied the presented approach to 22 teams from RoboCup 2011 in a fully automated fashion and the results show the validity of our approach. The synergetic effect of these individual actions would emerge as a team strategy and would reveal the quality of the team. This paper presents a novel framework that analyzes key agent behaviors in RoboCup Soccer Simulation 3D[6] using a set of pre-defined rules and classifies a team based upon these extracted team quality features. Team quality features were identified by a group of experts and extraction of these features is automated via a parser application. These features include team players positioning, frequency of players in attacking region, ball possession, average velocity of team players, frequency of kicks executed by teams, shots attempted towards goal etc. Once a team is classified upon its strength parameters then grouping of teams that play similar is done via clustering that would aid in counter strategy formulation against similar opponents. The major

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“…They are a useful support for setplays designers, but no domain expert knowledge is directly extracted from demonstration based on real situations [18] [24]. Automatic analysis of match logs was used to generalize a setplay in a simulated robotic soccer environment from a sequence of successful events [1]. A sequence of behaviors derived from coordinated positioning is formalized as a plan and incorporated to the team setplays library for future use.…”

Section: Related Workmentioning

confidence: 99%

A Dataset Schema for Cooperative Learning from Demonstration in Multi-robot Systems

Simões

Silva

Nogueira

2019

J Intell Robot Syst

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Multi-Agent Systems (MASs) have been used to solve complex problems which demand intelligent agents working together to reach the desired goals. These Agents should effectively synchronize their individual behaviors so that they can act as a team in a coordinated manner to achieve the common goal of the whole system. One of the main issues in MASs is the agents' coordination, being common domain experts observing MASs execution disapprove agents' decisions. Even if the MAS was designed using the best methods and tools for agents' coordination, this difference of decisions between experts and MAS is confirmed. Therefore, this paper proposes a new dataset schema to support learning the coordinated behavior in MASs from demonstration. The results of the proposed solution are validated in a Multi-Robot System (MRS) organizing a collection of new cooperative plans recommendations from the demonstration by domain experts. Keywords Multi-Agent System · Learning from Demonstration · Dataset · Coordination · Multi-robot plan · clustering PACS 07.05.Mh Dataset Schema for Cooperative Learning in a MRS 3Learning from Demonstration (LfD) has been used to learn robots basic skills or even very simple setplays in a MRS [11]. Although, there is no register of using LfD to learn complex setplays. Therefore, this work proposes using LfD to offer domain experts a chance to watch robotic soccer matches and suggest new setplays for each situation for which they think the MRS has made a bad decision.Section 3 presents the state-of-the-art for learning coordinated plans in MAS. One of the main issues in LfD for setplays is the nature of the dataset generated from the domain experts recommendation. Some features in this dataset are not of primitive types as scalars or strings, but some complex types, such as objects, structures, trees, etc. Thus, we also define a strategy presented in Section 2.4, to handle this kind of complex data.The proposed solution has a two-level dataset, detailed in Section 4. To assess the feasibility of using this dataset to support setplays learning, the Fuzzy C-Means (FCM) algorithm is used to organize setplays recommendations into clusters. The choice of the FCM algorithm is due to the imprecision inherent in the friendly interface proposed for use by experts to generate the recommendations of setplays. The suggestions from experts are organized in clusters to solve the semantic equivalence issue presented in Section 2. Section 5 describes the assessment process and its results. Section 6 has conclusion and future work descriptions.

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An automatic approach to extract goal plans from soccer simulated matches

Cited by 18 publications

References 24 publications

Development of flexible languages for scenario and team description in multirobot missions

Development of flexible languages for scenario and team description in multirobot missions

Opponent Classification in Robot Soccer

A Dataset Schema for Cooperative Learning from Demonstration in Multi-robot Systems

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