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Antonella Guzzo

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4 papers

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IJCAI Conference 2018 Conference Paper

Constrained Coalition Formation on Valuation Structures: Formal Framework, Applications, and Islands of Tractability (Extended Abstract)

Gianluigi Greco
Antonella Guzzo

Coalition structure generation is considered in a setting where feasible coalition structures must satisfy constraints of two different kinds modeled in terms of a valuation structure, which consists of a set of pivotal agents that are pairwise incompatible, plus an interaction graph prescribing that a coalition C can form only if the subgraph induced over the nodes/agents in C is connected. It is shown that valuation structures can be used to model a number of relevant problems in real-world applications. Moreover, complexity issues arising with them are studied, by focusing in particular on identifying islands of tractability based on topological properties of the underlying interaction graph. Stability issues on valuation structures are studied too.

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AIJ Journal 2017 Journal Article

Constrained coalition formation on valuation structures: Formal framework, applications, and islands of tractability

Gianluigi Greco
Antonella Guzzo

Coalition structure generation is the problem of partitioning the agents of a given environment into disjoint and exhaustive coalitions so that the whole available worth is maximized. While this problem has been classically studied in settings where all coalitions are allowed to form, it has been recently reconsidered in the literature moving from the observation that environments often forbid the formation of certain coalitions. By following this latter perspective, a model for coalition structure generation is proposed where constraints of two different kinds can be expressed simultaneously. Indeed, the model is based on the concept of valuation structure, which consists of a set of pivotal agents that are pairwise incompatible, plus an interaction graph prescribing that a coalition C can form only if the subgraph induced over the nodes/agents in C is connected. It is shown that valuation structures can be used to model a number of relevant problems arising in real-world application domains. Then, the complexity of coalition structure generation over valuation structures is studied, by assuming that the functions associating each coalition with its worth are given as input according to some compact encoding—rather than explicitly listing all exponentially-many associations. In particular, islands of tractability are identified based on the topological properties of the underlying interaction graphs and on suitable algebraic properties of the given worth functions. Finally, stability issues over valuation structures are studied too, by considering the core as the prototypical solution concept.

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EUMAS Conference 2016 Conference Paper

Coalition Formation with Logic-Based Agents

Gianluigi Greco
Antonella Guzzo

Abstract Coalition formation is studied in a setting where agents take part to a group decision-making scenario and where their preferences are expressed via weighted propositional logic, in particular by considering formulas consisting of conjunctions of literals only. Interactions among agents are constrained by an underlying social environment and each agent is associated with a specific social factor determining to which extent s/he prefers staying in a coalition with other agents. In particular, the utilities of the agents depend not only on their absolute preferences but also on the number of “neighbors” occurring with them in the coalition that emerged. Within this setting, the computational complexity of a number of relevant reasoning problems is studied, by charting a clear picture of the intrinsic difficulty of finding “agreements” in such social environments. Some restrictions leading to identify classes of tractable instances are discussed, too.

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ECAI Conference 2012 Conference Paper

Process Discovery via Precedence Constraints

Gianluigi Greco
Antonella Guzzo
Luigi Pontieri

A key task in process mining consists of building a graph of causal dependencies over process activities, which can then be used to derive more expressive models in some high-level modeling language. An approach to accomplish this task is presented where the learning process can exploit the background knowledge that, in many cases, is available to the analysts taking care of the process (re-)design. The method is based on encoding the information gathered from the log and the (possibly) given background knowledge in terms of precedence constraints, i. e. , constraints over the topology of the graphs. Learning algorithms are eventually formulated in terms of reasoning problems over precedence constraints, and the computational complexity of such problems is thoroughly analyzed by tracing their tractability frontier. The whole approach has been implemented in a prototype system leveraging a solid constraint programming platform, and results of experimental activity are reported.

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