Brenden Keyes, Mark Micire, Jill L. Drury and Holly A. Yanco (February 1st 2010). Improving Human-Robot Interaction through Interface Evolution, Human-Robot Interaction, Daisuke Chugo, IntechOpen, DOI: 10.5772/8140.

Richard Kelley, Alireza Tavakkoli, Christopher King, Monica Nicolescu and Mircea Nicolescu (February 1st 2010). Understanding Activities and Intentions for Human-Robot Interaction, Human-Robot Interaction, Daisuke Chugo, IntechOpen, DOI: 10.5772/8127.

Bernd Kleinjohann, Lisa Kleinjohann, Willi Richert and Claudius Stern (December 1st 2007). Integrating Autonomous Behaviour and Team Coordination into an Embedded Architecture, Robotic Soccer, Pedro Lima, IntechOpen, DOI: 10.5772/5135.

New Zealand

Effective coordination is a critical requirement for human teaming, and is increasingly needed in teams of humans and robots. Building on decades of work in the behavioral literature, we have implemented a computational framework for coordination based on Shared Mental Models (SMMs) in which robots use a distributed knowledge base to coordinate activity. We also built a novel system connecting the robotic architecture, DIARC, to the 3D simulation environment, Unity, to serve as an evaluation platform for the framework implementation, and also for more general explorations of teaming with autonomous robots. Using this platform, we ran a user study to evaluate the framework by comparing performance of teams in which the robots used SMMs with those that did not. We found that teams in which the robots used SMMs significantly outperformed those without SMMs. This represents the first empirical demonstration that SMMs can be successfully used by fully autonomous robots interacting in natural language to improve team performance, bringing robots a step closer to genuine teammate


AAMAS 2020

Toward Genuine Robot Teammates: Improving Human-Robot Team Performance Using Robot Shared Mental Models

**31st IEEE International Conference on Robot & Human Interactive Communication**
 This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.
 
 The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.
 
 **CONFERENCE THEME:**
 Social, asocial, and antisocial robots
 
 Robots are starting to populate our lives: at work, in our homes, hotels, shops. However, not everybody is comfortable with robots finding a place in society. For example, people may be afraid that robots will replace humans in their jobs. This belief is exacerbated by the media that, on one side, raise expectations of robots’ capabilities, while amplifying fears and worries on the other side.
 
 In the next years, due to the increasing request for interaction with people in social environments, academic research and market analyses predict that robots will become more and more “social”. But, at the same time, robots may incur the risk of being perceived as “asocial” as their pervasiveness in everyday environments may become an obstacle to social interaction between humans. Or they may even be rejected as “antisocial” if researchers do not take care to develop robots that behave according to the customs and habits of the communities where they are expected to operate.
 
 To prevent this from happening, robotic researchers can no longer take the luxury of being mere technicians. They must be capable of addressing all the challenges raised by future robot-populated communities in a multidisciplinary way. They need to share their knowledge with the public to make people aware of what robots really are, what expectations we can have from them, the risks we may incur, and the possible mitigation plans.
 
 While being open to any kind of contribution related to Human-Robot Interactive Communication, the RO-MAN 2022 conference will emphasize these aspects of robotic research. How can we create robots capable of being welcomed as our assistants and companions in society while, at the same time, not being perceived as “asocial” or “antisocial?” How can we create robots perceived as facilitators of the natural development of human beings and communities, rather than obstacles?
 
 Contributions from different disciplines, not exclusively related to technological development, will be welcomed as crucial to pave the way to global acceptance of future robots with an informed attitude.
 
 [![](https://assets.underline.io/uploads/markdown_image/1/image/af259abe105b3791c498ae1552d75710.png)](https://drive.google.com/file/d/18w73BZPB_JVOgcrcdZDqYpEupwVt4CnU/view?usp=sharing)

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[Register Here](http://www.smile.unina.it/ro-man2022/welcome-to-ieee-ro-man-2022/)

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IEEE RO-MAN 2022

31st IEEE International Conference on Robot & Human Interactive Communication
This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.

technical paper

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 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). 

An important aspect of multi-agent systems concerns the formation of coalitions that are stable or optimal in some well-defined way. The notion of popularity has recently received a lot of attention in this context. A partition is popular if there is no other partition in which more agents are better off than worse off. 
In 2019, a long-standing open problem concerning popularity was solved by proving that computing popular partitions in roommate games is NP-hard, even when preferences are strict. We show that this result breaks down when allowing for randomization: mixed popular partitions can be found efficiently via linear programming and a separation oracle. Mixed popular partitions are particularly attractive because they are guaranteed to exist in any coalition formation game.
Our result implies that one can efficiently verify whether a given partition in a roommate game is popular and that strongly popular partitions can be found in polynomial time (resolving an open problem). By contrast, we prove that both problems become computationally intractable when moving from coalitions of size 2 to coalitions of size 3, even when preferences are strict and globally ranked.


Finding and Recognizing Popular Coalition Structures

Designers of virtual agents have a combinatorically large space of choices for different media that comprise the look and behavior of their characters. We explore the systematic manipulation of animation quality, speech quality, and rendering style, and its impact on the perceptions of virtual agents in terms of naturalness, engagement, trust, credibility, and persuasion within a health counseling domain. The agent’s counseling behavior was based on live video footage of a human counselor. We conducted a between-subjects study that had 12 conditions. Character animation was varied between a static image, procedural animation using a gestuary, and manually rotoscoped animation. Voice quality was varied between recorded audio of the human counselor and synthetic speech. Character rendering style was varied between 3D-shaded realistic and toon-shaded. Prior studies indicate that people prefer and attribute more sociality to other people and agents when modalities are consistent in their level of quality. Thus, we hypothesize that people will be most affected by agents whose animation, voice, and rendering style are consistent, rather than the effects of channel quality being purely additive. Results indicate that natural animations and recorded voice are more appropriate for general acceptance, trust, and credibility of the agent, and how appropriate she seems for the task. However, our results indicate that for a brief health counseling task, animation might actually be distracting from the persuasive message, with the highest levels of persuasion found when the amount of agent animation is minimized.


Navigating the Combinatorics of Virtual Agent Design Space to Maximize Persuasion

We initiate the study of cloning in multiwinner elections, focusing
on single-transferable vote (STV), single-nontransferable vote
(SNTV), bloc, k-Borda, t-approval-CC, and Borda-CC. Transferring
the model of cloning due to Elkind et al. [15] from single-winner to
multiwinner elections, we consider decision problems describing
possible and necessary cloning in the zero-cost, the unit-cost, and
the general-cost model and study their computational complexity.
We show that, depending on the multiwinner voting rule and on
the cost model chosen, some of these cloning problems are in P,
some are NP-hard, and some of the latter (for which, in fact, already
winner determination is NP-hard) are fixed-parameter tractable.

The Complexity of Cloning Candidates in Multiwinner Elections

The hypergraph matching game is a cooperative game defined on a hypergraph such that the vertices are the players, and the characteristic function is the value of a maximum hypergraph matching on a hypergraph induced by a coalition. 
In this talk, we study a computationally tractable condition of the hypergraph matching game, called the convexity.
First, we prove that the problem of checking whether a given hypergraph matching game is convex or not is solvable in polynomial time.
Second, we prove that the Shapley value of a given convex hypergraph matching game is exactly computable in polynomial time.
Finally, we consider the fractional hypergraph matching game and prove that if the fractional hypergraph matching game is convex, then its characteristic function coincides with the characteristic function of the corresponding (integral) hypergraph matching game.

Convexity of Hypergraph Matching Game

We live in a world full of complex social systems. Achieving opti- mal control in a complex social system is challenging due to the difficulty in modeling and optimization. To capture the complex social system dynamics accurately and succinctly, we model the decision-making problem as a partially observable discrete event decision process. To withstand the curse of dimensionality in high- dimensional belief state spaces and to optimize the problem in an amenable searching space, we investigate the connections between the value function of a partially observable decision process and that in the corresponding fully-observable scenario, and reduce the optimal control of a partially observable discrete event decision process to a policy optimization with a specially formed fully ob- servable decision process and a belief state estimation. When tested in real-world transportation scenarios, in comparison with other state-of-the-art approaches, our proposed algorithm leads to the least average time on-road, the largest number of vehicles at work during work hours and the fewest training epochs to converge to the highest total rewards per episode.


Optimal Control in Partially Observable Complex Social Systems

We introduce a model for agent-environment systems where the agents are implemented via feed-forward ReLU neural networks and the environment is non-deterministic. We study the verification problem of such systems against CTL properties. We introduce a bounded fragment of CTL and show its usefulness in identifying shallow bugs in the system. We present a novel parallel algorithm for MILP-based verification of agent-environment systems, present an implementation, and report the experimental results obtained against a variant of the VerticalCAS use-case.


Formal Verification of Neural Agents in Non-deterministic Environments

Coupon has been a major marketing tool that promotes sales for new and repeated buyers and proven effective in numerous realistic scenarios. In this paper, we mainly focus on the problem of designing coupons to maximize the revenue in second-price auction. 

Firstly, we derive the dominant strategies of bidders if they are provided with coupons in second-price auction and prove that the revenue optimization problem with coupons for all the bidders is NP-complete. Secondly, we cast the problem of designing coupons to maximize revenue into a learning framework. With well-designed loss functions, we perform theoretical analysis of its properties and propose corresponding algorithms to solve the problem. Finally, with both synthetic data and industrial data, extensive experiments are conducted to demonstrate their effectiveness.


Learning to Design Coupons in Online Advertising Markets

Advertising has become a dominant source of revenue generation on the Internet. Billions of advertisement slots are sold via auctions. And there are many pricing methods ,e.g., CPM (cost-per-mille), CPC (cost-per-click), CPA (cost-per-action), OCPM (optimized cost-per-mille) and so on. In this paper, we study the OCPM method (i.e., advertisers bid for conversions while pay per mille) under VCG auction. However, automatically bid in each view to maximize advertisers' conversions while still meet their target cost-per-conversion in feeds is difficult. To deal with these difficulties, we propose a reinforcement learning framework, i.e., RSDRL (ROI-sensitive distributional reinforcement learning). By making full use of the characteristics of auction rules which are missed by other methods, we design a reward function to surrogate conversion events and a bid generation method based on theoretical results. We also provide some theoretical results to guide hyperparameter tuning. Last, we validate RSDRL on a large industrial dataset with millions of auctions. Plenty of experiments (both online and offline) are used to evaluate the performance of our framework and RSDRL yields substantially better results than compared algorithms.


Optimized Cost per Mille in Feeds Advertising

The plan recognition task is to infer an actor’s plan and goal given observations about its behavior. We submit that in some cases, for plan recognition to be effective and complete, it must appeal to a notion of epistemics to i) recognize epistemic goals, where the actor is trying to achieve some state of knowledge or belief; and ii) model the observer, and its knowledge of the actor, as first class elements of the plan recognition process. To this end, we formalize the notion of Epistemic Plan Recognition, which builds on two growing areas of research: epistemic planning and plan recognition. Our epistemic plan recognition specification appeals to an epistemic logic framework to represent agent beliefs. To realize our specification, we cast the epistemic plan recognition problem as an epistemic planning problem, whose solutions can be generated using existing epistemic planning tools. Finally, we evaluate our approach by utilizing and comparing existing epistemic planners on a diverse set of epistemic plan recognition problems.


Epistemic Plan Recognition

This paper focuses on two-sided matching where one side (a hospital or firm) is matched to the other side (a doctor or worker) so as to maximize a cardinal objective under general feasibility constraints. In a standard model, even though multiple doctors can be matched to a single hospital, a hospital has a responsive preference and a maximum quota. However, in practical applications, a hospital has some complicated cardinal preference and constraints. With such preferences (e.g., submodular) and constraints (e.g., knapsack or matroid intersection), stable matchings may fail to exist. This paper first determines the complexity of checking and computing stable matchings based on preference class and constraint class. Second, we establish a framework to analyze this problem on packing problems, and the framework enables us to access the wealth of online packing algorithms so that we construct approximately stable algorithms as a variant of generalized deferred acceptance algorithm. We further provide some inapproximability results. 


Toward Genuine Robot Teammates: Improving Human-Robot Team Performance Using Robot Shared Mental Models

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Finding and Recognizing Popular Coalition Structures

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Toward Genuine Robot Teammates: Improving Human-Robot Team Performance Using Robot Shared Mental Models

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Next from AAMAS 2020

Finding and Recognizing Popular Coalition Structures

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AugRE: Augmented Robot Environment to Facilitate Human-Robot Teaming and Communication

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