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Most studies investigating models and algorithms for distributed constraint optimization problems (DCOPs) assume messages arrive instantaneously or within a (short) bounded delay.

AAMAS 2021

Latency-Aware Local Search for Distributed Constraint Optimization

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

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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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This paper discusses the formalization of a norm enforcement mechanism for a regulative time constrained conditional normative framework. The norm representation captures both the condition and effect of a norm as situations and allows for rich temporal constraints between the times of the situations. As part of the enforcement process, a designated agent has an obligation within a time constraint to inform a liable agent of a reparative action they must take and the time constraint within which they must take it. That same agent must then monitor the compliance of the erring agent’s obligation to carry out the reparative action, meting out sanctions in the case of violation.

A Norm Enforcement Mechanism for a Time-Constrained Conditional Normative Framework (JAAMAS Track)

We describe an approach for constraining robot autonomy based on the robot's awareness of patterns of its human teammates' behaviors, rather than either ignoring its teammates (which is fast but dangerous) or inferring their plans (which is safer but slow). We evaluate this approach in a series of simulated problems where an unmanned ground vehicle and its human teammates must rapidly respond to a sudden context shift, and identify conditions that should be (purposely) met such that a pattern-aware approach is particularly effective compared to the alternatives.

On Teammate-Pattern-Aware Autonomy (JAAMAS Track)

In this work, we propose a framework that enables a human to teach a robot a new task by interactively providing it with unlabeled instructions. We ground the meaning of instruction signals in the task-learning process, and use them simultaneously for guiding the latter. We implement our framework as a modular architecture, named TICS (Task-Instruction-Contingency-Shaping) that combines different information sources: a predefined reward function, human evaluative feedback and unlabeled instructions. This approach provides a novel perspective for robotic task learning that lies between Reinforcement Learning and Supervised Learning paradigms. We evaluate our framework both in simulation and with a real robot. The experimental results demonstrate the effectiveness of our framework in accelerating the task-learning process and in reducing the number of required teaching signals.

Teaching a Robot with Unlabeled Instructions: The TICS Architecture (JAAMAS Track)

Multiagent system models do not typically encompass tools for tackling abnormal or exceptional situations. When a critical situation arises, and individual agents fail, the system as a whole usually fails, too. For realizing robust distributed systems, conceived as agent systems or organizations, it is necessary to keep a right level of situational awareness (of both agents and environment), through the introduction of the means for gathering and propagating feedback, upon which actions can be taken. This work proposes a notion of accountability that encompasses both a normative dimension, and a structural dimension, that serves for the purpose of robustness.

Robustness Based on Accountability in Multiagent Organizations

Community Health Workers (CHWs) form an important component of health-care systems globally, especially in low-resource settings. CHWs are often tasked with monitoring the health of and intervening on their patient cohort. Previous work has developed several classes of Restless Multi-Armed Bandits (RMABs) that are computationally tractable and indexable, a condition that guarantees asymptotic optimality, for solving such health monitoring and intervention problems (HMIPs). However, existing solutions to HMIPs fail to account for risk-sensitivity considerations of CHWs in the planning stage and may run the danger of ignoring some patients completely because they are deemed less valuable to intervene on. Additionally, these also rely on patients reporting their state of adherence accurately when intervened upon. Towards tackling these issues, our contributions in this paper are as follows: (1) We develop an RMAB solution to HMIPs that allows for reward functions that are monotone increasing, rather than linear, in the belief state and also supports a wider class of observations. (2) We prove theoretical guarantees on the asymptotic optimality of our algorithm for any arbitrary reward function. Additionally, we show that for the specific reward function considered in previous work, our theoretical conditions are stronger than the state-of-the-art guarantees. (3) We show the applicability of these new results for addressing the three issues pertaining to: risk-sensitive planning, equitable allocation and reliance on perfect observations as highlighted above. We evaluate these techniques on both simulated as well as real data from a prevalent CHW task of monitoring adherence of tuberculosis patients to their prescribed medication in Mumbai, India and show improved performance over the state-of-the-art. The simulation code is available at: https://github.com/AdityaMate/risk-aware-bandits.

Risk-Aware Interventions in Public Health: Planning with Restless Multi-Armed Bandits

In recent years there has been an increasing interest in ensuring that autonomous systems behave consistently with human values. A popular approach to this challenge is through the incorporation of norms that regulate behaviour in an ethical way. However, such norms must be effective at promoting the values we consider most important. In this work, we introduce a systematic methodology for the automated synthesis of parametric normative systems based on value promotion. We introduce the new concepts of 
 and 
 To quantify the effectiveness of norms at upholding the values we consider relevant, we adopt the value alignment indicator from a previously established framework. We apply our model to a toy system which we use to illustrate our approach from end to end.

Value-Guided Synthesis of Parametric Normative Systems

Online social networks fail to support users to adequately share co-owned content, which leads to privacy violations. Scholars proposed collaborative mechanisms to support users, but they did not satisfy one or more requirements needed according to empirical evidence in this domain, such as explainability, role-agnosticism, adaptability, and being utility- and value-driven. We present ELVIRA, an agent that supports multiuser privacy, whose design meets all these requirements. By considering the sharing preferences and the moral values of users, ELVIRA identifies the optimal sharing policy. Furthermore, ELVIRA justifies the optimality of the solution through explanations based on argumentation. We prove via simulations that ELVIRA provides solutions with the best trade-off between individual utility and value adherence. We also show through a user study that ELVIRA suggests solutions that are more acceptable than existing approaches and that its explanations are also more satisfactory.

ELVIRA: An Explainable Agent for Value and Utility-Driven Multiuser Privacy

Distributed Constraint Optimization Problems (DCOPs) are a suitable formulation for coordinating interactions (i.e. constraints) in cooperative multi-agent systems. The traditional DCOP model assumes that variables owned by the agents can take only discrete values and constraints' cost functions are defined for every possible value assignment of a set of variables. While this formulation is often reasonable, there are many applications where the decision variables are continuous-valued and constraints are in functional form. To overcome this limitation, Continuous DCOPs (C-DCOPs), an extension of the DCOPs model has been proposed that is able to formulate problems having continuous variables. The existing methods for solving C-DCOPs come with a huge computation and communication overhead. In this paper, we apply continuous non-linear optimization methods on Cooperative Constraint Approximation (CoCoA) algorithm, which is a non-iterative, fast incomplete local search approach for solving DCOPs. We empirically show that our algorithm is able to provide high-quality solutions at the expense of smaller communication cost and execution time compared to the state-of-the-art C-DCOP algorithms.

A Local Search Based Approach to Solve Continuous DCOPs

Gestures augment speech by performing a variety of communicative functions in humans and virtual agents, and are often related to speech by complex semantic, rhetorical, prosodic, and affective elements. In this paper we briefly present an architecture for human-like gesturing in virtual agents that is designed to realize complex speech-to-gesture mappings by exploiting existing machine-learning based parsing tools and techniques to extract these functional elements from speech. We then deeply explore the rhetorical branch of this architecture, objectively assessing specifically whether existing rhetorical parsing techniques can classify gestures into classes with distinct movement properties. To do this, we take a corpus of spontaneously generated gestures and correlate their movement to co-speech utterances. We cluster gestures based on their rhetorical properties, and then by their movement. Our objective analysis suggests that some rhetorical structures are identifiable by our movement features while others require further exploration. We explore possibilities behind these findings and propose future experiments that may further reveal nuances of the richness of the mapping between speech and motion. This work builds towards a real-time gesture generator which performs gestures that effectively convey rich communicative functions.

CMCF: An Architecture for Realtime Gesture Generation by Clustering Gestures by Motion and Communicative Function

In multi-agent systems the main process responsible for obtaining information about the environment is perception, generally this process is performed passively regardless the agent's intentional state. However, especially when inserted in the real world, a frequent problem is that agents have partial perception of the environment, failing to perceive some relevant information. To circumvent this problem, a solution is to actively take actions to perceive what is of interest to the agent, for example, in a computer vision system, the camera can be repositioned to have a better view of an object. This work aims to develop an active perception model integrated with the reasoning cycle of BDI agents. Experiments are performed using BDI agents with ROS to command unmanned aerial vehicles to analyze the benefits and impacts of using cognitive agents with active perception to program robot intelligence.

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A Norm Enforcement Mechanism for a Time-Constrained Conditional Normative Framework (JAAMAS Track)

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New Algorithms for Continuous Distributed Constraint Optimization Problems

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Next from AAMAS 2021

A Norm Enforcement Mechanism for a Time-Constrained Conditional Normative Framework (JAAMAS Track)

Similar lecture

New Algorithms for Continuous Distributed Constraint Optimization Problems

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads