Logics and strategic reasoning play a central role in multi-agent systems. Logics can be used, for instance, to express the agents' abilities, knowledge, and objectives. Strategic reasoning refers to algorithmic methods that allow for developing good behaviour for the agents of the system. At the intersection, we find logics that can express the existence of strategies or equilibria, and can be used to reason about them. The LAMAS&SR workshop merges two international workshops: LAMAS (Logical Aspects of Multi-Agent Systems), which focuses on all kinds of logical aspects of multi-agent systems from the perspectives of artificial intelligence, computer science, and game theory, and SR (Strategic Reasoning), devoted to all aspects of strategic reasoning in formal methods and AI.
LAMAS: The LAMAS workshop provides a meeting forum for the research community working on various logical aspects of multi-agent systems (MAS) from the perspectives of artificial intelligence, computer science, and game theory. It addresses the whole range of issues that arise in the context of using logic in MAS, from theoretical foundations to algorithmic methods and implemented tools. The workshop LAMAS has been regularly organised since 2002 and became the main annual event of the LAMAS research network.
SR: Strategic reasoning is a key topic in the multi-agent systems research area. The extensive literature in this field includes a number of logics used for reasoning about the strategic abilities of the agents in the system, but spans also game theory, decision theory or epistemic logics to name a few. The aim is to provide sound theoretical foundations and tools to tackle a variety of strategic problems in formal methods and artificial intelligence involving agents in adversarial settings. The SR workshop has been organised annually since 2013, often in co-location with the most important conferences in formal methods and AI.
LAMAS&SR: Over the years the communities and research themes of both workshops got closer and closer, with a significant overlap in the participants and organisers of both events. For this reason, the next editions of LAMAS and SR will be unified under the same flag, formally joining the two communities.
LAMAS&SR is interested in all topics related to logics and strategic reasoning in multi-agent systems, from theoretical foundations to algorithmic methods and implemented tools. The topics of the workshop include, but are not limited to:
Authors are invited to submit extended abstracts of 4 pages plus 1 page for references only, in the LNCS format. Both published and unpublished works are welcome. Submissions are subject to a single-blind review process (submissions should not be anonymous). Authors must submit their papers through the LAMAS&SR 2023 Easychair submission site at https://easychair.org/conferences/?conf=lamassr2023 as a single PDF file.
In the traditional maximum-flow problem, the goal is to transfer maximum flow in a network by directing, in each vertex in the network, incoming flow into outgoing edges. The problem has been extensively used in order to optimize the performance of networks in numerous application areas. The definition of the problem corresponds to a setting in which the authority has control on all vertices of the network. Today's computing environment involves parties that should be considered adversarial. We survey recent studies on flow games, which capture settings in which the vertices of the network are owned by different and selfish entities. We start with the case of two players, MAX (the authority), which aims at maximizing the flow, and MIN (the hostile environment), which aims at minimizing the flow. We argue that such flow games capture many modern settings, such as partially-controlled pipe or road systems or hybrid software-defined communication networks. We then continue to the special case where all vertices are owned by \minp. This case captures evacuation scenarios, where the goal is to maximize the flow that is guaranteed to travel in the most unfortunate routing decisions. Finally, we study the general case, of multiple players, each with her own target vertex. In all settings, we study the problems of finding the maximal flows, optimal strategies for the players, as well as stability and equilibrium inefficiency in the case of multi-player games. We discuss additional variants and their applications, and point to several interesting open problems. The talk is based on joint work with Shibashis Guha, Gal Vardi, and Moshe Y. Vardi.
We study a variant of the problem of synthesizing Mealy machines that enforce LTL specifications against all possible behaviours of the environment, including hostile ones. In the variant studied here, the user provides the high level LTL specification φ of the system to design, and a set E of examples of executions that the solution must produce. Our synthesis algorithm first generalizes the user-provided examples in E using tailored extensions of automata learning algorithms, while preserving realizability of φ. Second, it turns the (usually) incomplete Mealy machine obtained by the learning phase into a complete Mealy machine realizing φ. The examples are used to guide the synthesis procedure. We prove learnability guarantees of our algorithm and prove that our problem, while generalizing the classical LTL synthesis problem, matches its worst-case complexity. The additional cost of learning from E is even polynomial in the size of E and in the size of a symbolic representation of solutions that realize φ, computed by the synthesis tool ACACIA-BONZAI. We illustrate the practical interest of our approach on a set of examples. This is a joint work with Mrudula Balachander and Emmanuel Filiot.
The informal proceedings can be downloaded here.
