87,154 research outputs found
Integrating Partial Models of Network Normality via Cooperative Negotiation - An Approach to Development of Multiagent Intrusion Detection Systems
No full textUsing agents for developing intrusion detection systems can provide several advantages, including configurability, adaptability, scalability, and robustness. Almost all works in agent-based intrusion detection have considered agents as elements that perform specific tasks in the intrusion detection process. In this paper, we propose a novel way of using agents to solve one of the most pressing problems in intrusion detection: the definition of an accurate model of network normality. We consider agents as associated to partial models of network normality that harmonize their conflicts via cooperative negotiation. Experimental results show that the proposed approach is promising
A Game Theoretical Approach to Finding Optimal Strategies forPursuit Evasion in Grid Environments
No full textExploration strategies based on multi-criteria decision making for searching environments in rescue operations
No full textSome applications require autonomous robots to search an initially unknown environment for static targets, without any a priori information about environment structure and target locations. Targets can be human victims in search and rescue or materials in foraging. In these scenarios, the environment is incrementally discovered by the robots exploiting exploration strategies to move around in an autonomous and effective way. Most of the strategies proposed in literature are based on the idea of evaluating a number of candidate locations on the frontier between the known and the unknown portions of the environment according to ad hoc utility functions that combine different criteria. In this paper, we show some of the advantages of using a more theoretically-grounded approach, based on Multi-Criteria Decision Making (MCDM), to define exploration strategies for robots employed in search and rescue applications. We implemented some MCDM-based exploration strategies within an existing robot controller and we evaluated their performance in a simulated environment
A BIOCHEMICAL AND STRUCTURAL STUDY OF THE KINETOCHORE - CENTROMERE INTERFACE
No full textFaithful chromosome segregation during mitosis requires the dynamic interaction between spindle microtubules and kinetochores, multiprotein complexes built on centromeres.
A group of kinetochore proteins associates with centromeres throughout the cell cycle and is thus named constitutive centromere-associated network (CCAN). Biochemical and functional analyses indicate that CCAN proteins are organized in sub-complexes. However, the exact organization of these sub-complexes has not been fully elucidated to date.
The aim of my project has been the biochemical reconstitution of CCAN sub-complexes and their structural and functional characterization. In particular, this dissertation dwells upon the results I have obtained regarding three different but intrinsically related topics.
First, I present a biochemical and structural characterization of the CCAN protein CENP-M (centromere protein M), which displays the fold, but not the enzymatic activity of a G protein. In addition, I disclose its unprecedented role in the context of a quaternary complex with CENP-H, CENP-K and CENP-I and provide information about the spatial organization of this complex. The first steps towards an in vivo validation of these results are also described.
Second, I report the discovery of a direct interaction of CENP-H / CENP-K complex with CENP-C.
Third, I have been involved in establishing in the laboratory techniques for the in vitro reconstitution of recombinant nucleosomes. The production of material of good quality and quantity has recently been achieved, supporting the analysis of in vitro interactions between nucleosomes and kinetochore components. Specifically, I illustrate some preliminary observations concerning a direct interaction of Mis12 complex with nucleosomes
Multirobot Reconnection on Graphs: Problem, Complexity, and Algorithms
No full textIn several multirobot applications in which communication is limited, the mission could require the robots to iteratively take coordinated joint decisions on how to spread out in the environment and on how to reconnect with each other to share data and compute plans. Exploration and surveillance are examples of these applications. In this paper, we consider the problem of computing robots' paths on a graph-represented environment for restoring connections at minimum traveling cost. We call it the multirobot reconnection problem, we show its NP-hardness and hardness of approximation on some important classes of graphs, and we provide optimal and heuristic algorithms to solve it in practical settings. The techniques we propose are then exploited to derive a new efficient planning algorithm for a relevant connectivity-constrained multirobot planning problem addressed in the literature, the multirobot informative path planning with periodic connectivity problem
Defining effective exploration strategies for search and rescue applications with Multi-Criteria Decision Making
No full textAutonomous mobile robots are a promising tech-
nology for search and rescue scenarios, where an initially un-
known environment has to be explored to locate human victims.
Robots can exploit exploration strategies to autonomously move
around the environment. Most of the strategies proposed in
literature are based on the idea of evaluating a number of
candidate locations according to ad hoc utility functions that
combine different criteria. In this paper, we show some of the
advantages of using a more theoretically-grounded approach,
based on Multi-Criteria Decision Making (MCDM), to define
exploration strategies for robots employed in search and rescue
applications. We implemented our MCDM-based exploration
strategies within an existing robot controller and we evaluated
their performance in a simulated environment
Multi-agent path finding in configurable environments
No full textMulti-Agent Path Finding (MAPF) plays an important role in many real-life applications where autonomous agents must coordinate to reach their goals without collisions. MAPF problems often take place in structured environments that are usually assumed to be static and known in advance. In this paper, we introduce C-MAPF, i.e., MAPF in Configurable environments, a novel variant of the MAPF problem in which the environment is configurable, namely its structure and topology can be controlled within some given constraints. Consider, for instance, a warehouse logistics application: the environment can be changed (at least to some degree) by the managers of the warehouse, for example by re-arranging the positions of the shelves or by removing or adding temporary walls. We study the properties of the C-MAPF problem and we devise two algorithms for solving it, both based on Conflict-Based Search (CBS), a state-of-the-art MAPF algorithm. First, we present Parallel CBS (P-CBS), that searches for a solution by simultaneously considering all the possible configurations of the environment. We then present Abstract CBS (A-CBS), an extended version of the CBS algorithm that solves C-MAPF problems by introducing a new type of conflict on the allowable configurations of the environment. We prove that our solvers are both complete and optimal and we experimentally assess their performance in different settings
Robust Multi-Agent Pickup and Delivery with Delays
No full textMulti-Agent Pickup and Delivery (MAPD) is the problem of computing collision-free paths for a group of agents such that they can safely reach delivery locations from pickup ones. These locations are provided at runtime, making MAPD a combination between classical Multi-Agent Path Finding (MAPF) and online task assignment. Current algorithms for MAPD do not consider many of the practical issues encountered in real applications: real agents often do not follow the planned paths perfectly, and may be subject to delays and failures. In this paper, we study the problem of MAPD with delays, and we present two solution approaches that provide robustness guarantees by planning paths that limit the effects of imperfect execution. In particular, we introduce two algorithms, k-\mathbf{TP} and p-\mathbf{TP}, both based on a decentralized algorithm typically used to solve MAPD, Token Passing (TP), which offer deterministic and probabilistic guarantees, respectively. Experimentally, we compare our algorithms against a version of TP enriched with online replanning. k-\mathbf{TP} and p-\mathbf{TP} provide robust solutions, significantly reducing the number of replans caused by delays, with little or no increase in solution cost and running time
Asynchronous multi-robot patrolling against intrusions in arbitrary topologies
No full textUse of game theoretical models to derive randomized mobile robot patrolling strategies has recently received a growing attention. We focus on the problem of patrolling environments with arbitrary topologies using multiple robots. We address two important issues currently open in the literature. We determine the smallest number of robots needed to patrol a given environment and we compute the optimal patrolling strategies along several coordination dimensions. Finally, we experimentally evaluate the proposed techniques. Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org)
Multi-Agent Pickup and Delivery with Task Probability Distribution
No full textMulti-Agent Pickup and Delivery (MAPD) consists in completing a set of tasks by having agents move to the pickup location and then to the delivery location of each task. In MAPD, new tasks are dynamically added to the system throughout its lifetime and existing algorithms usually assume either complete ignorance or full knowledge about the position and the time at which future tasks will appear until they are actually added to the system. This paper introduces a novel MAPD problem in which a spatial and temporal probability distribution of future tasks is known and defines algorithms that take advantage of this knowledge to reduce the average time required to execute tasks. In particular, we build on an existing MAPD algorithm, Token Passing (TP), proposing different ways to exploit a given task probability distribution. Experiments show that these methods can have a positive impact on the time required to complete the tasks
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