Formalization and Verification ofGroup Behavior Interactions
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.
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