Formalization and Verification Of

Formalization and Verification of

Group Behavior Interactions

Abstract

Group behavior interactions, such as multirobot teamwork and group communications in social networks, are widely seen in both natural, social, and artificial behaviorrelated applications. Behavior interactions in a group are often associated with varying coupling relationships, for instance, conjunction or disjunction. Such coupling relationships challenge existing behavior representation methods, because they involve multiple behaviors from different actors, constraints on the interactions, and behavior evolution. In addition, the quality of behavior interactions are not checked through verification techniques. In this paper, we propose an ontology-based behavior modeling and checking system (OntoB for short) to explicitly represent and verify complex behavior relationships, aggregations, and constraints. The OntoB system provides both a visual behavior model and an abstract behavior tuple to capture behavioral elements, as well as building blocks. It formalizes various intra-coupled interactions (behaviors conducted by the same actor) via transition systems (TSs), and inter-coupled behavior aggregations (behaviors conducted by different actors) from temporal, inferential, and party-based perspectives. OntoB converts a behavior-oriented application into a TS and temporal logic formulas for further verification and refinement. We demonstrate and evaluate the effectiveness of the OntoB in modeling multirobot behaviors and their interactions in the Robocup soccer competition game. We show, that the OntoB system can effectively model complex behavior interactions, verify and refine the modeling of complex group behavior interactions in a sound manner

Existing System

We illustrate the Existing System behavior modeling and checking framework in terms of the case study: the multirobot soccer game in Section this multirobot architecture is composed of n robots, including k retrievers. All the robots interact with the environment. During the interactions, they perceive the world, perform actions, and communicate messages with one another for a collaboration. A team of retriever robots RCs and robot players Ords communicate with one another and try to put the ball in the opponent’s goal as frequently as possible, while the opponent’s robots have the same goal. When a new situation arises, a distinguished set of k retrievers RCs take charge of selecting cases from a case space and then inform the rest of the ordinary robot players Ords. Also as the coordinators, RCs send messages (msg) to all Ords and instruct them to conduct the corresponding actions. If timeout expires, or messages or cases are lost in the interactions, Ords abort the executions at any moment based on their own perceptions. Below, we discuss all modules in the proposed OntoB in detail, and illustrate them by concrete examples from the above case study system. More comprehensively, Section IX provides an experimental demonstration for the whole process of OntoB by using the multirobot soccer system.

Proposed System

in this papaer we generate ,The behavior visual and formal descriptors complement each other to support the complete formulation of behavior interactions. The previous sections focus on revealing the explicit description of the behavior elements in a visual way. In this section, we first introduce an abstract behavior model by specifying the concepts and relationships involved. Further, we propose a formal behavior model to represent the various relationships based on the ontology specification. Inspired by the abstract behavior model , several relevant definitions are given, followed by illustration of their use in modeling behaviors in the robot soccer game system.There are multible behavors action our behavior model. All the reviews and commends visible for the multiple user from the behavior model,and can find and buy the new product is easyest way for the behavioral model of formalization and verification group behavor .

Conclusion

The most advantage of this paper is that it is the first work on behavior to systematically and flexibly address the concept of couple behaviors in a solid and generic manner, which can also be respected as a starting point for other researchers to follow this promising area as pointed out in Fig. 11. Currently, we are working on the extension of logic expressions for constraints, a behavior algebra to consolidate the techniques for modeling and checking complex behaviors, and behavior aggregation rules for the divergence and convergence of complex couplings. The context-sensitive coupled behaviors are worth exploring and investigating. Overall, the analysis of group behavior interactions brings about great challenges and opportunities in many aspects such as representing, checking, reasoning, learning behavior couplings and interactions, as well as mining behavior interaction patterns.

REFERENCES

[1] T. D. Wilson and C. Walsh, “Information behavior: An interdisciplinary

perspective,” Inf. Process. Manage., vol. 33, no. 4, pp. 551–572,

Jul. 1997.

[2] L. Getoor and B. E. Taskar, Introduction to Statistical Relational

Learning. Cambridge, MA, USA: MIT Press, 2007.

[3] T. Hogg and G. Szabo, “Diversity of online community activities,” in

Proc. 19th ACM Conf. Hypertext Hypermedia (HT), Pittsburgh, PA,

USA, Jun. 2008, pp. 227–228.

[4] L. Cao and P. Yu, “Behavior informatics: An informatics perspective for

behavior studies,” IEEE Intell. Inf. Bull., vol. 10, no. 1, pp. 6–11, Dec.

2009.

[5] R. Ros and M. Veloso, “Executing multi-robot cases through a single

coordinator,” in Proc. 6th Int. Conf. Auton. Agents Multiagent Syst.,

Honolulu, HI, USA, May 2007, pp. 1272–1274.

[6] L. Cao, Y. Ou, and P. Yu, “Coupled behavior analysis with applications,”

IEEE Trans. Knowl. Data Eng., vol. 24, no. 8, pp. 1378–1392,

Aug. 2012.

[7] L. Cao, “In-depth behavior understanding and use: The behavior informatics

approach,” Inf. Sci., vol. 180, no. 17, pp. 3067–3085, Sep. 2010.

[8] H. L. Kim, J. G. Breslin, S. Decker, and H. G. Kim, “Mining and representing

user interests: The case of tagging practices,” IEEE Trans. Syst.,

Man, Cybern. A, Syst., Humans, vol. 41, no. 4, pp. 683–692, Jul. 2011.