Distributed Fair Allocation of Indivisible Goods

International audienceDistributed mechanisms for allocating indivisible goods are mechanisms lacking central control, in which agents can locally agree on deals to exchange some of the goods in their possession. We study convergence properties for such distributed mechanisms when used as fair division procedures. Specifically, we identify sets of assumptions under which any sequence of deals meeting certain conditions will converge to a proportionally fair allocation and to an envy-free allocation, respectively. We also introduce an extension of the basic framework where agents are vertices of a graph representing a social network that constrains which agents can interact with which other agents, and we prove a similar convergence result for envy-freeness in this context. Finally, when not all assumptions guaranteeing envy-freeness are satisfied, we may want to minimise the degree of envy exhibited by an outcome. To this end, we introduce a generic framework for measuring the degree of envy in a society and establish the computational complexity of checking whether a given scenario allows for a deal that is beneficial to every agent involved and that will reduce overall envy

Game Theory Models for Multi-Robot Patrolling of Infraestructures

Abstract This work is focused on the problem of performing multi‐robot patrolling for infrastructure security applications in order to protect a known environment at critical facilities. Thus, given a set of robots and a set of points of interest, the patrolling task consists of constantly visiting these points at irregular time intervals for security purposes. Current existing solutions for these types of applications are predictable and inflexible. Moreover, most of the previous centralized and deterministic solutions and only few efforts have been made to integrate dynamic methods. Therefore, the development of new dynamic and decentralized collaborative approaches in order to solve the aforementioned problem by implementing learning models from Game Theory. The model selected in this work that includes belief‐based and reinforcement models as special cases is called Experience‐Weighted Attraction. The problem has been defined using concepts of Graph Theory to represent the environment in order to work with such Game Theory techniques. Finally, the proposed methods have been evaluated experimentally by using a patrolling simulator. The results obtained have been compared with previous availabl

Reaching Envy-free States in Distributed Negotiation Settings

Mechanisms for dividing a set of goods amongst a number of autonomous agents need to balance ef- ficiency and fairness requirements. A common in- terpretation of fairness is envy-freeness, while ef- ficiency is usually understood as yielding maximal overall utility. We show how to set up a distributed negotiation framework that will allow a group of agents to reach an allocation of goods that is both efficient and envy-free.ou

Allocation in Practice

How do we allocate scarcere sources? How do we fairly allocate costs? These are two pressing challenges facing society today. I discuss two recent projects at NICTA concerning resource and cost allocation. In the first, we have been working with FoodBank Local, a social startup working in collaboration with food bank charities around the world to optimise the logistics of collecting and distributing donated food. Before we can distribute this food, we must decide how to allocate it to different charities and food kitchens. This gives rise to a fair division problem with several new dimensions, rarely considered in the literature. In the second, we have been looking at cost allocation within the distribution network of a large multinational company. This also has several new dimensions rarely considered in the literature.Comment: To appear in Proc. of 37th edition of the German Conference on Artificial Intelligence (KI 2014), Springer LNC

Endocannabinoids Generated by Ca2+ or by Metabotropic Glutamate Receptors Appear to Arise from Different Pools of Diacylglycerol Lipase

The identity and subcellular sources of endocannabinoids (eCBs) will shape their ability to affect synaptic transmission and, ultimately, behavior. Recent discoveries support the conclusion that 2-arachidonoyl glycerol, 2-AG, is the major signaling eCB, however, some important issues remain open. 2-AG can be synthesized by a mechanism that is strictly Ca2+-dependent, and another that is initiated by G-protein coupled receptors (GPCRs) and facilitated by Ca2+. An important question is whether or not the 2-AG in these cases is synthesized by the same pool of diacylglycerol lipase alpha (DAGLα). Using whole-cell voltage-clamp techniques in CA1 pyramidal cells in acute in vitro rat hippocampal slices, we investigated two mechanistically distinct eCB-mediated responses to address this issue. We now report that pharmacological inhibitors of DGLα have quantitatively different effects on eCB-mediated responses triggered by different stimuli, suggesting that functional, and perhaps physical, distinctions among pools of DAGLα exist

DLK1 Is a Somato-Dendritic Protein Expressed in Hypothalamic Arginine-Vasopressin and Oxytocin Neurons

Delta-Like 1 Homolog, Dlk1, is a paternally imprinted gene encoding a transmembrane protein involved in the differentiation of several cell types. After birth, Dlk1 expression decreases substantially in all tissues except endocrine glands. Dlk1 deletion in mice results in pre-natal and post-natal growth deficiency, mild obesity, facial abnormalities, and abnormal skeletal development, suggesting involvement of Dlk1 in perinatal survival, normal growth and homeostasis of fat deposition. A neuroendocrine function has also been suggested for DLK1 but never characterised. To evaluate the neuroendocrine function of DLK1, we first characterised Dlk1 expression in mouse hypothalamus and then studied post-natal variations of the hypothalamic expression. Western Blot analysis of adult mouse hypothalamus protein extracts showed that Dlk1 was expressed almost exclusively as a soluble protein produced by cleavage of the extracellular domain. Immunohistochemistry showed neuronal DLK1 expression in the suprachiasmatic (SCN), supraoptic (SON), paraventricular (PVN), arcuate (ARC), dorsomedial (DMN) and lateral hypothalamic (LH) nuclei. DLK1 was expressed in the dendrites and perikarya of arginine-vasopressin neurons in PVN, SCN and SON and in oxytocin neurons in PVN and SON. These findings suggest a role for DLK1 in the post-natal development of hypothalamic functions, most notably those regulated by the arginine-vasopressin and oxytocin systems

A biophysical model of endocannabinoid-mediated short term depression in hippocampal inhibition

Memories are believed to be represented in the synaptic pathways of vastly interconnected networks of neurons. The plasticity of synapses, that is, their strengthening and weakening depending on neuronal activity, is believed to be the basis of learning and establishing memories. An increasing number of studies indicate that endocannabinoids have a widespread action on brain function through modulation of synap–tic transmission and plasticity. Recent experimental studies have characterised the role of endocannabinoids in mediating both short- and long-term synaptic plasticity in various brain regions including the hippocampus, a brain region strongly associated with cognitive functions, such as learning and memory. Here, we present a biophysically plausible model of cannabinoid retrograde signalling at the synaptic level and investigate how this signalling mediates depolarisation induced suppression of inhibition (DSI), a prominent form of shortterm synaptic depression in inhibitory transmission in hippocampus. The model successfully captures many of the key characteristics of DSI in the hippocampus, as observed experimentally, with a minimal yet sufficient mathematical description of the major signalling molecules and cascades involved. More specifically, this model serves as a framework to test hypotheses on the factors determining the variability of DSI and investigate under which conditions it can be evoked. The model reveals the frequency and duration bands in which the post-synaptic cell can be sufficiently stimulated to elicit DSI. Moreover, the model provides key insights on how the state of the inhibitory cell modulates DSI according to its firing rate and relative timing to the post-synaptic activation. Thus, it provides concrete suggestions to further investigate experimentally how DSI modulates and is modulated by neuronal activity in the brain. Importantly, this model serves as a stepping stone for future deciphering of the role of endocannabinoids in synaptic transmission as a feedback mechanism both at synaptic and network level