Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning

Efficient information dissemination is crucial for supporting critical operations across domains like disaster response, autonomous vehicles, and sensor networks. This paper introduces a Multi-Agent Reinforcement Learning (MARL) approach as a significant step forward in achieving more decentralized, efficient, and collaborative information dissemination. We propose a Partially Observable Stochastic Game (POSG) formulation for information dissemination empowering each agent to decide on message forwarding independently, based on the observation of their one-hop neighborhood. This constitutes a significant paradigm shift from heuristics currently employed in real-world broadcast protocols. Our novel approach harnesses Graph Convolutional Reinforcement Learning and Graph Attention Networks (GATs) with dynamic attention to capture essential network features. We propose two approaches, L-DyAN and HL-DyAN, which differ in terms of the information exchanged among agents. Our experimental results show that our trained policies outperform existing methods, including the state-of-the-art heuristic, in terms of network coverage as well as communication overhead on dynamic networks of varying density and behavior.Comment: 13 pages, 5 figures, 4 table

Bribery in voting with soft constraints

Abstract We consider a multi-agent scenario where a collection of agents needs to select a common decision from a large set of decisions over which they express their preferences. This decision set has a combinatorial structure, that is, each decision is an element of the Cartesian product of the domains of some variables. Agents express their preferences over the decisions via soft constraints. We consider both sequential preference aggregation methods (they aggregate the preferences over one variable at a time) and one-step methods and we study the computational complexity of influencing them through bribery. We prove that bribery is NPcomplete for the sequential aggregation methods (based on Plurality, Approval, and Borda) for most of the cost schemes we defined, while it is polynomial for one-step Plurality

uncertain, and conditional statements

In this thesis we consider reasoning with preferences in a number of different AI problems. We start with a specific area as, temporal reasoning, for which there is specialized reasoning machinery. In particular, we consider quantitative temporal constraints and we add preferences in such a context. We discuss the complexity for solving temporal constraint satisfaction problems with preferences in general, and we identify a tractable subclass: simple temporal problems with semi-convex preference functions. For this subclass, we consider finding optimal solutions with respect to the (fuzzy) maximin criterion and to the Pareto criterion. In the first case, we propose two solvers based on two different approaches. One enforces the notion of path consistency adapted to deal also with preferences. We show that enforcing such a property is sufficient to solve an STPP and it is a polynomial time algorithm. The second solver, reduces the problem to that of solving several hard constraint problems. We show that also this solver is polynomial. We have also designed a solver that finds Pareto optimal solutions. It applies one of the solvers for fuzzy-optimal solutions several times, to problems obtained from the original one by changing some constraints. Also this solver is polynomial. We have implemented and tested the solvers for fuzzy optimals. The tests, performed on randomly generated problems, show that the second solver is much faster, although it allows to represent less general preferences. We also consider the problem of actually obtaining the preferences on all the temporal constraints. Practically, it may not be feasible to retrieve all such preferences by a user. We, vii thus, apply a machine learning technique, inductive learning, to overcome this problem. In partic..

Temporal Constraint Satisfaction Problems

Simple Temporal Problems (STPs) are a restriction of the framework of Temporal Constraint Satisfaction Problems, tractable in polynomial time. Their expressiveness has been extended independently in two ways. First, to account for uncontrollable events, to Simple Temporal Problems with Uncertainty (STPUs). Second, more recently, to account for soft temporal preferences, to Simple Temporal Problems with Preferences (STPPs). The motivation for both extensions is from real-life problems; and indeed such problems may well necessitate both preferences and uncertainty. Our research proposes the study of Simple Temporal Problems with Preferences and Uncertainty (STPPUs), and puts forward two notions of controllability for their resolution

Dynamic consistency of fuzzy conditional temporal problems

Conditional Temporal Problems (CTPs) can deal simultaneously with uncertainty and temporal constraints, allowing for the representation of temporal and conditional plans. CTPPs generalize CTPs by adding preferences to the temporal constraints and by allowing fuzzy thresholds for the occurrence of some events. Here we focus on dynamic consistency of CTPPs, the most useful notion of consistency in practice. We describe an algorithm which allows for testing if a CTPP is dynamically consistent and we study its complexity. Simple Temporal Problems with Preferences and Uncertainty (STPPUs) are another formalism to model temporal constraints where preference and uncertainty coexist. While uncertainty is CTPPs is modeled via conditions on the execution of variables, in STPPUs it is modelled by means of events whose occurrence time is not known. We consider the relation between CTPPs and STPPUs and we show that the former framework is at least as expressive as the second one. Such a result is obtained by providing a polynomial mapping from STPPUs to CTPPs

Preferences in Constraint Satisfaction Problems

We review constraint-based approaches to handle preferences. We start by defining the main notions of constraint programming, then give various concepts of soft constraints and show how they can be used to model quantitative preferences. We then consider how soft constraints can be adapted to handle other forms of preferences, such as bipolar, qualitative, and temporal preferences. Finally, we describe how AI techniques such as abstraction, explanation generation, machine learning, and preference elicitation, can be useful in modelling and solving soft constraints

Simple Temporal Problems with Preferences and Uncertainty

Simple Temporal Problems (STPs) are a restriction of the framework of Temporal Constraint Satisfaction Problems, tractable in polynomial time. Their expressiveness has been extended independently in two ways. First, to account for uncontrollable events, to Simple Temporal Problems with Uncertainty (STPUs). Second, more recently, to account for soft temporal preferences, to Simple Temporal Problems with Preferences (STPPs). The motivation for both extensions is from real-life problems; and indeed such problems may well necessitate both preferences and uncertainty. Our research proposes the study of Simple Temporal Problems with Preferences and Uncertainty (STPPUs), and puts forward two notions of controllability for their resolution

Special issue on temporal representation and reasoning (TIME’13)

SCOPUS: ed.jinfo:eu-repo/semantics/publishe