Runtime revision of sanctions in normative multi-agent systems
- PDF / 2,623,576 Bytes
- 54 Pages / 439.37 x 666.142 pts Page_size
- 101 Downloads / 160 Views
(2020) 34:43
Runtime revision of sanctions in normative multi‑agent systems Davide Dell’Anna1 · Mehdi Dastani1 · Fabiano Dalpiaz1
© The Author(s) 2020
Abstract To achieve system-level properties of a multiagent system, the behavior of individual agents should be controlled and coordinated. One way to control agents without limiting their autonomy is to enforce norms by means of sanctions. The dynamicity and unpredictability of the agents’ interactions in uncertain environments, however, make it hard for designers to specify norms that will guarantee the achievement of the system-level objectives in every operating context. In this paper, we propose a runtime mechanism for the automated revision of norms by altering their sanctions. We use a Bayesian Network to learn, from system execution data, the relationship between the obedience/violation of the norms and the achievement of the system-level objectives. By combining the knowledge acquired at runtime with an estimation of the preferences of rational agents, we devise heuristic strategies that automatically revise the sanctions of the enforced norms. We evaluate our heuristics using a traffic simulator and we show that our mechanism is able to quickly identify optimal revisions of the initially enforced norms. Keywords Multiagent systems · Norm revision · Norm enforcement
1 Introduction Multiagent systems (MASs) comprise autonomous agents that interact in a shared environment [57]. To achieve the system-level objectives of a MAS, the behavior of the autonomous agents should be controlled and coordinated [11]. For example, a smart traffic system is a MAS that includes autonomous agents like cars, traffic lights, etc. The objectives of the system include avoiding the occurrence of traffic jams as well as minimizing the number of accidents.
* Davide Dell’Anna [email protected] Mehdi Dastani [email protected] Fabiano Dalpiaz [email protected] 1
Utrecht University, Utrecht, The Netherlands
13
Vol.:(0123456789)
43
Page 2 of 54
Autonomous Agents and Multi-Agent Systems
(2020) 34:43
One way to control the behavior of the agents in a MAS without limiting their autonomy is norm enforcement [1, 47]. Norm enforcement via sanctions is traditionally contrasted with norm regimentation; the latter alternative prevents the agents from reaching certain states of affairs. For example, in a smart traffic system, a regimentation strategy is to close a road to prevent cars from entering that road, while a sanctioning strategy is to impose sanctions on cars that drive through the road. Due to the dynamicity and unpredictability of the behaviours of interacting agents in uncertain environments, it is difficult for the designers who engineer a MAS to specify norms that, when enforced, will guarantee the achievement of system-level objectives in every operating context. To cope with this issue, the enforced norms need to be revised at runtime. Existing research has investigated the offline revision of the enforced norms [3], proposed logics that support norm change [4, 3
Data Loading...
