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A crucial challenge for robotic systems is to figure out the behavior of another agent. We cast this challenge as a problem of causal inference. The presentation introduces the complications of inferring causal relationships from human behavior under two conditions that question the approach&#39;s feasibility. Subsequently, the presentation describes how we instantiated the approach and its feasibility despite the challenging conditions enforced.

AAMAS 2022

Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities

human behavior

robotics

causal inference

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
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EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.


A crucial challenge for robotic systems is to figure out the behavior of another agent. We cast this challenge as a problem of causal inference. The presentation introduces the complications of inferring causal relationships from human behavior under two conditions that question the approach's feasibility. Subsequently, the presentation describes how we instantiated the approach and its feasibility despite the challenging conditions enforced.

technical paper

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).

A ticket is required to attend this event, please register using the link below:

https://aamas2022-conference.auckland.ac.nz/attending/registration/

Registration Is Required

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).


As more and more hard and complex procedures are being automated with the aid of artificial intelligence, the need for humans to understand the rationale behind AI decisions becomes imperative. Adequate explanations for decisions made by an intelligent system do not just help describing how the system works, they also earn users' trust. In this work we focus on a general methodology for justifying why certain teams are formed and others are not by a team formation algorithm (TFA). Specifically, we introduce an algorithm that wraps up any existing TFA and builds justifications regarding the teams formed by such TFA. This is done without modifying the TFA in any way. Our algorithm offers a user a collection of commonly-asked questions within a team formation scenario and builds justifications as contrastive explanations. Finally, we conduct an empirical evaluation to measure the quality of the justifications of our algorithm.

Building contrastive explanations for multi-agent team formation


Multi-Agent Path Finding (MAPF) and Multi-Robot Motion Planning (MRMP) are complex problems to solve, analyze and build algorithms for. Automatically-generated explanations of algorithm output, by improving human understanding of the underlying problems and algorithms, could potentially alleviate this issue. Explanations are contextual, however, and thus developers need a good understanding of the questions that can be made about algorithm output, the kinds of explanations that exist, and the potential users and uses of explanations in MAPF/MRMP applications. In this paper we provide a first step towards establishing a taxonomy of explanations, and a list of requirements for the development of explainable MAPF/MRMP planners. We use interviews and a questionnaire with expert developers and industry practitioners to identify the kinds of questions, explanations, users, uses, and requirements of explanations that should be considered in the design of such explainable planners. Our insights cover a diverse set of applications of MAPF/MRMP: warehouse automation, computer games, mining, and manufacturing.

Explainability in Multi-Agent Path/Motion Planning: User-study-driven taxonomy and requirements


Trust is a fundamental aspect of teamwork in Human-Agent Teams(HATs). Trust violations are an inevitable aspect of the cycle of trust,so effective trust repair strategies are needed to ensure durable andsuccessful team performance. This study explores the effectivenessof four trust repair strategies. In a first-person shooter resemblingHAT task, a trust violation was provoked when the robotic agentfailed to detect an approaching enemy. After this, the agent offeredan apology composed of an explanation and/or an expression ofregret (either one alone, both or neither). Our results indicated thatexpressing regret was crucial for effective trust repair, and thattrust repair was most effective when the apology contained bothcomponents.

Trust repair in human-agent teams: the effectiveness ofexplanations and expressing regret


We investigate the use of multi-agent allocation techniques on problems related to Earth observation scenarios with multiple users and satellites. We focus on the problem of coordinating users having reserved exclusive orbit portions and one central planner having several requests that may use some intervals of these exclusives. We define this problem as Earth Observation Satellite Constellation Scheduling Problem (EOSCSP) and map it to a Mixed Integer Linear Program. As to solve EOSCSP, we propose market-based techniques and a distributed problem solving technique based on Distributed Constraint Optimization (DCOP), where agents cooperate to allocate requests without sharing their own schedules. These contributions are experimentally evaluated on randomly generated EOSCSP instances based on real large-scale or highly conflicting observation order books.

Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions


Wind farms suffer from the so-called wake effects: when some of the turbines are located in the wakes of other turbines, their power output is substantially reduced. These losses can be partially mitigated via actively changing the yaw from the individually optimal direction. Many of the existing wake control techniques have two major limitations: they use simplified wake models to optimize the control strategy, and they assume that the atmospheric conditions remain stable. In this paper, we address these limitations by applying reinforcement learning (RL) to the active wake control problem. RL forgoes the wake model entirely and learns the optimal control strategy based on the observed atmospheric conditions and the reward signal, in this case the power output of the farm. It also accounts for random transitions in the observations, such as turbulent fluctuations in the wind. To evaluate the benefits of RL for active wake control, we implement a simulator based on the state-of-the-art FLORIS model in the OpenAI gym format, making it readily available to the RL community. Next, we propose three different state-action representations of the active wake control problem and investigate their effect on the performance of RL-based wake control. Finally, we compare RL to a state-of-the-art wake control strategy based on FLORIS and show that RL is less sensitive to changes in unobservable data.

Deep Reinforcement Learning for Active Wake Control


In agent programming and engineering, learning has been explored so far mainly as an effective AI technique to tackle specific problems, improving agents' (and MAS as well) adaptability and autonomy at runtime. 

In this paper we consider a broader role of learning, impacting on the full spectrum of agent programming and engineering - from design to runtime. In particular, we introduce a perspective in which agent development is conceived as a kind developmental learning process of the agent itself. In that view, a software agent "is born" with some core domain-independent learning capabilities at the architectural level and is grown up in a proper learning environment to acquire the proper skills to be able then to achieve its designed objectives once it will be deployed, analogously to children in the human case.

The idea relies on - on the one hand - existing literature about how learning has been injected in cognitive architectures as developed in cognitive science (e.g. SOAR, LIDA), as well as in agent architecture such as BDI. On the other hand, the idea drawn inspiration from human science, in particular the literature investigating the role of learning in human development, children in particular, and the impact of capabilities on human autonomy and practical reasoning.

From a software engineering point of view, this idea is meant to be a conceptual baseline to explore general-purpose architectures and methodologies - eventually extending existing ones, such as the BDI - to seamlessly and systematically integrate both parts engineered by developers (e.g. plans) and parts learnt by the agent, eventually adopting different learning strategies.

Main challenges of this view include devising the model and features for the primitive domain-independent learning system to be injected in agent core architecture and reasoning cycle, to support both developmental/continual learning, and how to structure the learning process occurring in the learning environment during the design/development stage. In the paper, causal reasoning and learning are identified and discussed as a main reference to this purpose, to be integrated with practical reasoning as implemented by modern agent architectures and technologies.

"Go to the Children": Rethinking to Intelligent Agent Design and Programming in a Developmental Learning Perspective


In this paper we propose a novel approach to education and learning, in which each human being is associated with a digital agent that takes care of proactively searching for opportunities by monitoring job markets, and consulting requirements, educational options and challenges. Then, it adjusts the current educational program and coordinates the pathway facilitating the optimal desired outcome. We envision a Distributed Educational Agent Network (DEAN) of connected, trustworthy educational agents that securely communicate with each other to collectively carry out the above-mentioned tasks, discussing the benefits and current and future challenges associated with the implementation of the proposed agent-assisted educational approach.

Agent-Assisted Life-Long Education and Learning

We develop a game theoretic model of malware protection using the state-of-the-art sandbox method, to characterize and compute optimal defense strategies for anti-malware. We model the strategic interaction between developers of malware (M) and anti-malware (AM) as a two player game, where AM commits to a strategy of generating sandbox environments, and M responds by choosing to either attack or hide malicious activity based on the environment it senses. We characterize the condition for AM to protect all its machines, and propose two frameworks to solve the for optimal AM strategy in other cases. First, we provide quadratically constrained quadratic program (QCQP)-based optimization framework to solve for the optimal AM strategy. Second, we model the problem using generative adversarial networks where M uses a discriminator to detect its current environments, and AM uses a generator to generate sandboxes to defeat M's discriminator.

Anti-Malware Sandbox Games

We show that Game Description Language (GDL) can be used to describe some of the most commonly used test-beds in the automated negotiations literature, namely Genius and Colored Trails. This opens up an entirely new, declarative, approach to automated negotiation, in which a single algorithm can negotiate over a very broad class of different negotiation domains. We formally prove that the set of possible agreements of any negotiation domain from Genius (either linear or non-linear) can be modeled as a set of strategies over a deterministic extensive-form game that can be described efficiently in GDL. Furthermore, we show experimentally that, given only this GDL description, we can explore the agreement space efficiently using entirely generic domain-independent algorithms. In addition, we show that the same also holds for negotiation domains in the Colored Trails framework. This means we have the basic ingredients to implement a single negotiating agent that is capable of negotiating over many different kinds of negotiation domains, including Genius and Colored Trails.

GDL as a Unifying Domain Description Language for Declarative Automated Negotiation (Extended Abstract)

We propose and study a general framework for modelling and formal reasoning about multi-agent systems and, in particular, multi-stage games where both quantitative and qualitative objectives and constraints are involved. Our models enrich concurrent game models with payoffs and guards on actions associated with each state of the model. We propose a quantitative extension of the logic ATL* that enables the combination of quantitative and qualitative reasoning. We illustrate the framework with some examples and then consider the model-checking problems arising in our framework and establish some general undecidability and decidability results for them.

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Building contrastive explanations for multi-agent team formation

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Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

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Building contrastive explanations for multi-agent team formation

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Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

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