United Kingdom

Business models rooted in shared economy, electrification, and automation are transforming urban mobility. Accounting for how these transformations interact is crucial if synergies are to be exploited. In this paper, we focus on how a cost-effective charging infrastructure for e-mobility can support the emergence of shared, autonomous mobility. This study addresses the problem of siting and sizing of charging stations for a fleet of shared autonomous electric vehicles (SAEVs). We develop a hybrid simulation-optimization model to find locations and numbers of chargers needed to serve charging demands. Our agent-based model provides an enhanced representation of SAEV operations allowing for smart charging and vehicle cruising when parking/charging is not available. Also, we model charging station placement as full covering optimization and solve the location-allocation problem simultaneously. Finally, we employ real-world trip data from ShareNow in Berlin to evaluate our approach for realistic demand patterns under different charging strategies and fleet sizes. The results show that charging station locations depend mostly on the spatial distribution of installation costs and charging demands. Moreover, charging strategies and fleet size affect the charging patterns and the required number of chargers as well as fleet performance.

AAMAS 2021

Siting and Sizing of Charging Infrastructure for Shared Autonomous Electric Fleets

#### Welcome to the 60th IEEE International Reliability Physics Symposium (IRPS)!

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**General Chair Welcome Message**

Welcome to IRPS 2022! When the IRPS Management Committee began making plans for Dallas, we recognized the strong desire by the reliability physics community to return to a face-to-face symposium that we last had in Monterey in 2019. But as time progressed, we recognized that travel restrictions were not being lifted as rapidly as we anticipated. Rather than leave out those of you who could not attend in person, we adopted a hybrid format allowing for both remote attendance and remote presentations. This is a first for IRPS and I am confident it will be a success for all.

With this new hybrid format, the symposium will return to the original five-day format with Tutorials and Year in Review on the first two days. The Technical Program, Keynote Talks, Workshops and Poster session will be held on the following three days. All of these (except Workshops and Posters) will be available via real-time streaming in the US Central Time zone with virtual attendees having Q&A access to the presenters. For those virtual attendees who cannot watch the live presentations, the material will be available on demand to all attendees for a month after the symposium with the ability to communicate with the authors.

In 2020, we were planning on co-hosting the International ESD Workshop (IEW) ESD workshop when the COVID-19 pandemic hit. For 2022, IEW will be seamlessly embedded into the IRPS Program and will sponsor three tutorials, an invited talk, two technical sessions and two evening workshops focused on ESD and latch-up. And we continue our
collaboration with our sister conference, European Symposium on Reliability of Electron Devices Failure Physics and Analysis (ESREF) by hosting their Best Paper and the International Integrated Reliability Workshop (IIRW) by hosting their Best Student Paper.

Our primary objective is to maintain the symposium’s leadership role in the reliability physics of microelectronic devices by attracting leading researchers to present their work and providing attendees the best place to access this information. With all the buzz in the media about whether Moore’s Law has hit its limit, IRPS has been promoting expertise developed in silicon-based reliability over the past sixty years and expanding into new material areas. This year, eight of the technical sessions are devoted to reliability in topics outside of traditional silicon, including wide bandgap GaN/SiC, emerging memory and neuromorphic computing. I hope our ability to draw these communities together will accelerate the cross-fertilization of ideas. 

The Technical Program consists of four keynote speakers, twenty-three invited speakers and eighty-seven oral presentations organized in thirty technical sessions on eighteen reliability topics. Over two evenings, there will be ten Workshops and sixty-six Poster presentations that are designed to allow in person attendees and experts to interact
with one another. With eighteen tutorials covering topics ranging from fundamental transistor and memory reliability all the way up to automotive and complex system-level reliability, this year’s symposium provides an excellent learning opportunity for engineers and scientists new to reliability as well as experienced individuals wanting to explore new
areas. The Year in Review, which serves as a summary of research in the past year, will cover 3D packaging, emerging memory and aging aware circuit designs.

Finally, I would like to express my extreme gratitude to our patrons and exhibitors, whose support makes IRPS possible.
And to all of you, our in person and virtual attendees, for supporting this symposium for sixty years!

**Charles W. Slayman, General Chair** 
**of 2022 IRPS Management Committee**
 
**About IRPS**

The IEEE International Reliability Physics Symposium (IRPS) is the premiere conference for engineers and scientists to present new and original work in the area of microelectronics reliability. Drawing participants from the United States, Europe, Asia, and all parts of the world, IRPS seeks to understand the reliability of semiconductor devices, integrated circuits, and microelectronic assemblies through an improved understanding of both the physics of failure as well as the application environment. IRPS provides numerous opportunities for attendees to increase their knowledge and understanding of all aspects of microelectronics reliability, and to meet and network with reliability colleagues from around the world.

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IRPS 2022 Main Conference 

IRPS provides numerous opportunities for attendees to increase their knowledge and understanding of all aspects of microelectronics reliability. It is also an outstanding chance to meet and network with reliability colleagues from around the world.

technical paper

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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Expressing attack-defence trees (ADTrees) in a multi-agent setting allows for studying a new aspect of security scenarios, namely how the number of agents and their task assignment impact the performance of attacking and defending strategies executed by agent coalitions. Our tool ADT2AMAS allows for transforming ADTrees into extended asynchronous multi-agent systems and computing an optimal schedule with the minimal number of agents. ADT2AMAS is integrated within the graphical verification platform CosyVerif, but can also be run standalone.

ADT2AMAS: Managing Agents in Attack-Defence Scenarios

We present a substantially expanded version of our tool STV for strategy synthesis and verification of strategic abilities. The new version adds user-definable models and support for model reduction through partial order reduction and checking for bisimulation.

STV+Reductions: Towards Practical Verification of Strategic Ability Using Model Reductions

In cooperative human decision-making, agreements are often not total; a partial degree of agreement is sufficient to commit to a decision and move on, as long as one is somewhat confident that the involved parties are likely to stand by their commitment in the future, given no drastic unexpected changes. In this work, we introduce models that allow autonomous agents to reach such agreements, using abstract argumentation as the underlying model.

Toward Consistent Agreement Approximation in Abstract Argumentation and Beyond

Non-stationary environments are challenging for reinforcement learning algorithms. If the state transition and/or reward functions change based on latent factors, the agent is effectively tasked with optimizing a behavior that maximizes performance over a possibly infinite random sequence of Markov Decision Processes (MDPs), each of which drawn from some unknown distribution. We call each such MDP a context. Most related works make strong assumptions such as knowledge about the distribution over contexts, the existence of pre-training phases, or a priori  knowledge about the number, sequence, or boundaries between contexts. We introduce an algorithm that efficiently learns policies in non-stationary environments. It analyzes a possibly infinite stream of data and computes, in real-time, high-confidence change-point detection statistics that reflect whether novel, specialized policies need to be created and deployed to tackle novel contexts, or whether previously-optimized ones might be reused. We show that (i)  this algorithm minimizes the delay until unforeseen changes to a context are detected, thereby allowing for rapid responses; and (ii) it bounds the rate of false alarm, which is important in order to minimize regret. Our method constructs a mixture model composed of a (possibly infinite) ensemble of probabilistic dynamics predictors that model the different modes of the distribution over underlying latent MDPs. We evaluate our algorithm on high-dimensional continuous reinforcement learning problems and show that it outperforms state-of-the-art (model-free and model-based) RL algorithms, as well as state-of-the-art meta-learning methods specially designed to deal with non-stationarity.

Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection

Turn-based games on graphs are games where the states are controlled by one and only one player who decides which edge to follow. Each player has a temporal objective that he tries to achieve. One player is the designated 'controller', whose objective captures the desirable outcomes of the whole system. Cooperative rational synthesis is the problem of computing a Nash equilibrium that satisfies the controller's objective. In this paper, we tackle this problem in the context of a commons, where each action has a cost or a benefit on one shared common pool energy resource. The paper investigates the problem of synthesising the controller in a commons such that there exists an individually rational behaviour of all the agents in the commons that satisfies the controller's objective and does not deplete the resource. We consider two types of agents: careless and careful. Careless agents only care for their temporal objective, while careful agents also pay attention not to deplete the system's resource. We solve the problem of cooperative rational synthesis in these games, focusing on parity objectives.

Rational Synthesis in the Commons with Careless and Careful Agents

We introduce a novel method to aggregate Bipolar Argumentation (BA) Frameworks expressing opinions by different parties in debates. We use Bipolar Assumption-based Argumentation (ABA) as an all-encompassing formalism for BA under different semantics. By leveraging on recent results on judgement aggregation in Social Choice Theory, we prove several preservation results, both positive and negative, for relevant properties of Bipolar ABA.

Aggregating Bipolar Opinions

Team formation is the problem of deploying the least expensive team of agents while covering a set of skills. Once a team has been formed, some of the agents considered at start may be finally defective and some skills may become uncovered. Two solution concepts have been recently introduced to deal with this issue in a proactive manner: one may form a team which is robust to changes so that after some agent losses, all skills remain covered; or one may opt for a recoverable team, i.e., it can be "repaired" in the worst case by hiring new agents while keeping the overall deployment cost minimal. In this paper, we introduce the problem of partially robust team formation (PR-TF). Partial robustness is a weaker form of robustness which guarantees a certain degree of skill coverage after some agents are lost. We analyze the computational complexity of PR-TF, and provide a complete algorithm for it. The performance of our algorithm is empirically compared with the existing methods for robust and recoverable team formation, on a number of existing benchmarks and some newly introduced ones. Partial robustness is shown to be an interesting trade-off notion between (full) robustness and recoverability in terms of computational efficiency, skill coverage guarantees after agent losses, and repairability.

Partial Robustness in Team Formation: Bridging the Gap between Robustness and Resilience

We study mechanism design for nonexcludable and excludable binary public project problems. We aim to maximize the expected number of consumers and the expected agents' welfare. For the nonexcludable public project model, we identify a sufficient condition on the prior distribution for the conservative equal costs mechanism to be the optimal strategy-proof and individually rational mechanism. For general distributions, we propose a dynamic program that solves for the optimal mechanism. For the excludable public project model, we identify a similar sufficient condition for the serial cost sharing mechanism to be optimal for 2 and 3 agents. We derive a numerical upper bound. Experiments show that for several common distributions, the serial cost sharing mechanism is close to optimality.

Mechanism Design for Public Projects via Neural Networks

In AI, Multi-Agent Systems are able to model many kind of collective behavior and have therefore a wide range of application. In this paper, we propose a logical framework (Logic of "Inferable") which enable reasoning about whether a group of agents can perform an action, highlighting the concepts of cost of actions and of budget that agents have available to perform actions. The focus is on modeling the group dynamics of cooperative agents.

A Logic of Inferable in Multi-Agent Systems with Budget and Costs

The goal of this work is to propose a framework for representing and reasoning about the rules governing a combinatorial exchange. Such a framework is at first interest as long as we want to build up digital marketplaces based on auction, a widely used mechanism for automated transactions. Hence the framework should fulfill two requirements: (i) it should enable bidders to express their bids on combinations of goods and (ii) it should allow describing the rules governing some market, namely the legal bids, the allocation and payment rules. To do so, we define a logical language in the spirit of the Game Description Language: the Combinatorial Exchange Description Language is the first language for describing combinatorial exchange in a logical framework. The contribution is two-fold: first, we illustrate the general dimension by representing different kinds of protocols, and second, we show how to reason about auction properties in this machine-processable language.

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ADT2AMAS: Managing Agents in Attack-Defence Scenarios

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A Deeper Understanding of Well Charging Reliability with Circuit Relevant Test Structures

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Next from AAMAS 2021

ADT2AMAS: Managing Agents in Attack-Defence Scenarios

Similar lecture

A Deeper Understanding of Well Charging Reliability with Circuit Relevant Test Structures

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads