United Kingdom

Many real-world scenarios involve teams of agents that have to coordinate their actions to reach a shared goal. We focus on the setting in which a team of agents faces an opponent in a zero-sum, imperfect-information game. Team members can coordinate their strategies before the beginning of the game, but are unable to communicate during the playing phase of the game. This is the case, for example, in Bridge, collusion in poker, and collusion in bidding.

AAMAS 2021

Multi-Agent Coordination in Adversarial Environments through Signal Mediated Strategies

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**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

We hope that you enjoy the conference this year!<br><br>

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**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

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CogSci 2021

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

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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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)

This extended abstract summarises the contributions from the journal article Fisher et al. (Autonomous Agents and Multi Agent Systems 35, 1 (2021), 8).

Summarising a Framework for the Certification of Reliable Autonomous Systems (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)

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

In cooperative multi-agent reinforcement learning, a collection of agents learns to interact in a shared environment to achieve a common goal. We propose the use of reward machines (RM) -- Mealy machines used as structured representations of reward functions -- to encode the team's task. The proposed novel interpretation of RMs in the multi-agent setting explicitly encodes required teammate interdependencies, allowing the team-level task to be decomposed into sub-tasks for individual agents. We define such a notion of RM decomposition and present algorithmically verifiable conditions guaranteeing that distributed completion of the sub-tasks leads to team behavior accomplishing the original task. This framework for task decomposition provides a natural approach to decentralized learning: agents may learn to accomplish their sub-tasks while observing only their local state and abstracted representations of their teammates. We accordingly propose a decentralized q-learning algorithm. Furthermore, in the case of undiscounted rewards, we use local value functions to derive lower and upper bounds for the global value function corresponding to the team task. Experimental results in three discrete settings exemplify the effectiveness of the proposed RM decomposition approach, which converges to a successful team policy an order of magnitude faster than a centralized learner and significantly outperforms hierarchical and independent q-learning approaches.

Reward Machines for Cooperative Multi-Agent Reinforcement Learning

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

Latency-Aware Local Search for Distributed Constraint Optimization

The agile software development life cycle is widely used in industry today due to its highly flexible and iterative processes that facilitate rapid prototyping. There has been recent work in bringing concepts and processes from agile methodologies to agent-oriented software engineering (AOSE). We contribute to this effort by presenting in this paper a novel approach to capturing requirements of agent systems in AOSE using and extending agile concepts. In this paper, we propose to adopt and extend the well-known concept of 
 to facilitate the development of agent systems. We introduce a novel concept, 
, that defines requirements from the perspective of the system. These System Stories are refinements of User Stories and provide more intuitive mappings to agent concepts in the design and implementation. We show how our approach allows better traceability of requirements between stories and the different software development artifacts. We validate our proposal with a feature-based comparison to recent related work, and a preliminary user evaluation based on a drone simulation of a simple search and rescue case study.

User and System Stories: An Agile Approach for Managing Requirements in AOSE

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.

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On Teammate-Pattern-Aware Autonomy (JAAMAS Track)

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Learning from Agentic Actions: Modelling Causal Inference from Intention

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Next from AAMAS 2021

On Teammate-Pattern-Aware Autonomy (JAAMAS Track)

Similar lecture

Learning from Agentic Actions: Modelling Causal Inference from Intention

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads