Abstract
A norm regulates the run-time behavior of the agent with the action and condition to trigger the action. Because of the incomplete understanding of the world, the result of the action may be different for the same agent with the `same' context, or unintended different context. To study this phenomenon, the classical norm definition is extended from the condition-action pair to cover the expectation, in order to verify the result of the action. The Norm evolution can be defined as a gradual process which makes a norm more complete and effective. In the terminology of evolution, a norm is called mutated if the result contradicts to the expectation, i.e. at least one of the expected conditions is invalid. At run-time, norms are executed in series. A mutation brings new knowledge to the following states and might affect the later execution of the norms. Such knowledge provides will help the norm designer to complete the definitions. A mutation based norm evolution (Mone) method is proposed in this paper to detect the mutations, to propagate the evidence and to crossover the norms for completeness. The method is formalized in the Description Logic, and implemented with the algorithms for mutation detection and norm crossover. The case study illustrates the Description Logic $\mathcal {ALCI}$ of the method and shows the potential to evolve the norms autonomously in the Blackboard system, GBBopen.
Highlights
One of the most famous multi-agent system frameworks is the blackboard based system (BBS) whose history goes back to 1980’s [1]
As far as we known from the state-of-the-art, there is no publications focusing on this kind of mutation detection and usage so we do not have a comparative experiment with other methods
LOW-LEVEL NORM In this paper, a description logic is used to represent the scenario of low level norms without considerations of temporal constraint, permission/obligation/prohibition, event, and many other features of modern BBS based applications
Summary
One of the most famous multi-agent system frameworks is the blackboard based system (BBS) whose history goes back to 1980’s [1]. The common solution is to evaluate and place a priority on the conflicting norms It is different from the Mone method in that we focus on the different behaviors of the same norm on multiple executions and explore the possible reasons inherited from the execution results of other norms that are mutated and generate expected knowledge useful for the evolution. A Norm regulates the agent that given the trigger state, the execution result of the action should be the Expectation. The agent can execute a series of norms multiple times in the ‘same’ context, such as in the motivating example, the robot sweeps the floor everyday. There exists some condition C as the result of the execution of the norm R = (T , E ) in β ahead of R, such that ¬hasCond(E , C) mutates R , and C is inherited to the norm R that the state ∃hasCond.({C} ∃inState.T ) triggers R and prevents R from mutation. The expectation of the norm is regarded as the precise result from the norm action
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