Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/39460
Title: Graph Transformation Games for Negotiating Features
Authors: Alabdullatif, Mohammed Abdulrahman Ahmed
Supervisors: Heckel, Reiko
Erlebach, Thomas
First Published: 7-Mar-2017
Award date: 7-Mar-2017
Abstract: The success of e-commerce applications and services depends on the outcomes of interactions between the provider of the products or services and its requestors. The flexibility of these agents to negotiate features of the products or services traded is an important characteristic of face-to-face business interactions, but is often missing in the online world. Flexibility is needed to discuss preferences and constraints in order to determine a solution that benefits both parties. Game theory is a natural framework in which to pose such problems. This thesis is concerned with a proposal-based negotiation: through which a service provider and requestor interact by exchanging proposals. In particular, we propose negotiation games based on feature models to design the flexible business interactions. Feature models are used to represent service configurations in order to support the variability of negotiated services, which increases the flexibility of the negotiators’ interactions. We introduce graph transformation games to implement and analyse our negotiation games, modelling the negotiation of features by representing the state of the game by a graph and the moves of the players by graph transformation rules. We propose two analyses of our graph transformation games in order to explore different negotiation strategies. Firstly, we analyse our graph transformation games as extensive-form games, in which backward induction technique is used to solve the game and determine the optimal strategies for the negotiators at each state of the game. Secondly, we analyse our graph transformation games as two-player turn-based stochastic games using the PRISM-games model checker. We define single-objective and multi-objective properties in order to generate optimal strategies for the players. To evaluate our approach, we applied it to a selection of feature models in order to test the scalability of the graph transformation games’ generation and analysis time.
Links: http://hdl.handle.net/2381/39460
Type: Thesis
Rights: Copyright © the author. All rights reserved.
Appears in Collections:Leicester Theses
Theses, Dept. of Informatics

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