PRE2022 3 Group12

Problem Statement:

Society is currently faced with an ageing population. By around 2040, it is expected that one-quarter of the population will be aged 65 years or older. Compared to today, the size of this group of people will have increased by about 1.2 million people by 2040, all while the number of people working (in the age group 20 to 64 years old) will stay roughly the same. This means a large shortage of healthcare workers will arise, causing some elderly to not receive all care they might be expecting. One important aspect of this care that might easily be overlooked are ways to combat their loneliness. This is often prevalent among the elderly, especially those aged 75 years or older. One possible way to battle loneliness is to provide activities. However, with the reduced availability of care, it will become harder for healthcare workers to provide these activities. In these circumstances, robots can be used to support the workers.

Users:

Our design provides people with an opportunity to play physical card games without the need for other players. This is beneficial for anyone who is for some reason unable or uninclined to play with others. While it is great to have many potential people that are able to use the product, it also results in a large and ill-defined target group. In order to combat this general target group as well as form a starting point for the design and make it feasible considering the size of this project, a subset of the target group is taken. This new target group focuses on elderly people.

The target group of elderly people is chosen as they are generally assumed to have more difficulties with technology. It’s therefore expected that if the elderly people are able to properly use and understand the product, the younger generations will be able to do so as well.

We hope to increase the Quality of Life (QoL) of the elderly by creating this product. For example when they are unable to visit others, or unable to have visitors, they can still play with the robot and enjoy a game of cards.

User Requirements:

Due to their age, most elderly have increased problems with their sight, hearing, or motor skills. Therefore, it is important that the design has options built in to deal with this. For example, an easy-to-read font and text size, clear and loud audio implementations, and a lightweight and easy-to-move design.

Through our literary research, it was also noted that elderly people often experience more difficulties when learning something new. Because of this, it is assumed that using concepts that the elderly are already familiar with, or at least similar to those, is better as they will understand and learn them faster. Therefore, we should choose a game that is easy to understand and known by elderly people. As well as implement a simple interface and design.

Other aspects that could be added in order to improve the user experience, but are not necessary. Are the implementation of motivational messages during the game and multiple difficulty settings as a balance between ability and difficulty is important.

To engage the users’ more while playing with the robot, it is important that the robot has a competitive nature. Instead of having a robot that is relationship driven.

Literature Study:

Card games and AI

Policy-Based Inference in Trick-Taking Card Games

Summary:

This paper describes how an opponent model is used for inference in trick-taking card games, like Contract Bridge, Skat, and Hearts. These card games introduce uncertainty by featuring a large amount of private information, which becomes known after a long sequence of actions. Therefore, the number of histories is exponentially large in the action sequence length and extremely large information sets get created.

Deterministic search algorithms such as Perfect Information Monte Carlo and Information Set Monte Carlo Tree Search have been employed. However, due to non-locality, deterministic search has been heavily criticized. To deal with these issues,  inference helps by biasing state samples so that they are more realistic with respect to the opponent’s actions. Therefore, inference is a central concept in imperfect information games and plays a key role in the performance of deterministic search algorithms. This involves an opponent model to determine unknown information based on the action sequence.

The paper describes how policy-based inference was employed for Skat, which is a 3-player trick-taking card game and is played using a 32-card deck where cards 2 through 6 from each suit are removed from the standard 52-card deck. The actions are determined by three factors each having a certain probability. Firstly,  the world relates to chance nodes in dealing and can be directly computed. Secondly, our own actions with a probability of 1 since we choose actions leading to a given state with full knowledge of our strategy. Lastly, a given state within the information set due to other players’ actions can only be determined perfectly if we have access to the other players’ policies. However, there are two issues. Firstly, we do not have access to the other players’ policies or they are computationally too expensive to model. Secondly, the number of states in the information set can be quite large. For these reasons, the authors suggest sampling the worlds and normalizing the distribution over the subset of states.

The article concludes that policy-based Inference appears to provide much stronger inference than other methods such as Kermit Inference and Card Location Inference. Furthermore, the authors conclude that sampling card configurations are more effective than sampling states. Lastly, it is suggested to experiment with heuristics that allow the algorithm to find states that are highly unlikely and discard them to improve the performance.

Reference:

D. Rebstock, C. Solinas, M. Buro and N. R. Sturtevant, "Policy Based Inference in Trick-Taking Card Games," 2019 IEEE Conference on Games (CoG), London, UK, 2019, pp. 1-8, doi: 10.1109/CIG.2019.8848029.

A Social Robot as a Card Game Player

Summary:

In this paper, it is investigated how a social robot player that is able to play the card game with social behaviours towards its partner and its opponents can be built. The authors state that generally, social robots can contribute with new ways of creating socially engaging interactions with humans in entertainment contexts.  For instance, physical embodiment can provide a more immersive user experience, an improved game feedback and a more believable social interaction. However, for multi-player games played in the physical world, the social environment becomes even more relevant. Therefore, the paper finds an answer to the question of how people will perceive a social robot player compared to human standards and if people are willing to trust a social robot to be their partner in a team game.

For their research, the authors employ a social robot, which is able to express emotions, provide spoken feedback, and respond socially, for the team card game called Sueca, which is a non-deterministic game and is considered an imperfect information game. Furthermore, this paper explores how the algorithm’s parametrizations affect the performance-time configuration.

The robot has two modules, the game module responsible for choosing moves and the social module responsible for social behaviour. The game module evolved over three stages. Firstly, a rule-based player was created that replicates the general gameplay strategy of non-professional human players. The performance of this agent was found to be similar to the performance of human players. Secondly, the Perfect Information Monte-Carlo (PIMC) algorithm was applied, which is a suitable algorithm for partially observable environments. With PMIC, the hidden information is sampled several times and the best move is computed by solving exactly or heuristically in each perfect information game by use of the MinMax algorithm. However, PMIC has two disadvantages, namely strategy fusion and non-locality. On top of that, success of PMIC depends on three game properties, namely leaf correlation, bias and disambiguation factor. Thirdly, a hybrid approach was employed to respect the time constraint without bounding the depth of the search. Here PIMC was only used from a certain tick on. Up to that tick, a stochastic version of the rule-based strategy was adopted.

Additionally, the social module ensures that the robotic player engages in social interactions using both verbal and non-verbal behaviours. The emotional and social behaviours of the robot were built by An emotional agent framework (FAtiMA) allowing the robot to not only play competitively but also respond emotionally in a natural manner by emotional appraisal, social and emotional behaviours.

The experiment was performed by using the autonomous robot EMotive headY System (EMYS) over a multi-touch table using physical cards. Furthermore, the approach was tested by a user study to compare the levels of trust that participants attributed to the robot. The results showed that human players increased their trust in the robot as their game partners. Furthermore, results have shown that the robot team had a winning rate of 60%.

The authors conclude that the social robot showed similar results in terms of trust from a human partner as humans partners,  indicating that we trust a robot to be our game partner in a card game. This also shows the success of the social human-robot interaction.

Reference:

Correia, F., Alves-Oliveira, P., Ribeiro, T., Melo, F., & Paiva, A. (2021). A Social Robot as a Card Game Player. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 13(1), 23-29. https://doi.org/10.1609/aiide.v13i1.12936

Exploring the Entertainment Value of Playing Games with a Humanoid Robot

Summary:

Reference: Johnson, D.O., Cuijpers, R.H., Pollmann, K. et al. Exploring the Entertainment Value of Playing Games with a Humanoid Robot. Int J of Soc Robotics 8, 247–269 (2016). https://doi.org/10.1007/s12369-015-0331-x

Object recognition

Papers that Luke will summarize:

- extension://elhekieabhbkpmcefcoobjddigjcaadp/https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=d0646c9b7c8111e1c7ed5b34827324c322c52638

Problem, users and existing products

Designing social games for children and older adults: Two related case studies

Summary:

This article focuses on making a new board game, more engaging by adding technology. While focusing on two groups, namely children and elderly. The reasoning for combining the paper games and technology is, is that paper games have two important elements which games on computers either don’t have, or have less. So would the combination be able to still have the tangible parts, such as moving an actual piece across the board. As well as that the combination has more social interaction between players, as they can actually look each other in the face.

They based the gameboard versions for the children and elderly on what they usually play and their own suggestions. For the elderly they noticed that bank, poker and Black Jack were popular card games.

With the technology the playboard becomes more dynamic, sounds are added to create suspense for example, and the technology adds a bit of uncertainty to game. Both groups appreciated the uncertainty element, and found the version with the technology to be more engaging than the same paper version.

Reference:

A. A. Mahmud, O. Mubin, S. Shahid, J. Martens (2010). “Designing social games for children and older adults: Two related case studies”. https://doi.org/10.1016/j.entcom.2010.09.001

Friends or Foes? Socioemotional Support and Gaze Behaviors in Mixed Groups of Humans and Robots

Summary:

The study tries to figure out how humans and robots behave when interacting in small groups, with both humans and robots. And whether this could give new insights to how social robots should be designed. One robot that is used in the experiments is relationship driven and cooperative, namely Glin+. Whereas the other robot, Emys-, is competitive. For the experiment they focused on two elements, the eye gaze and the socioemotional support.

They concluded that participants looked more often at Glin+ when it was their partner, while they looked more at Emys- when it was their opponent. This could possibly be, because they consider Emys- to be more of a “threat” for their goal in the game, as Emys- is more competitive.

They also noticed that Emys- got more support when Emys- was a opponent, than Glin+ got when Glin+ was an opponent. This came as a surprise towards the authors, as they had expected that Emys- competitive behavior would have lead towards rivalry and would have undermined pro-social motivation. They suspect this might be, because the participants possibly wanted to appease Emys-.

The authors’ last conclusion is that partners supported each other more, and that the participants showed more support towards other humans.

Reference:

R. Oliveira, P. Arriaga, P. Alves-Oliveira, F. Correia, S. Petisca, A. Paiva (2018). “Friends or Foes? Socioemotional Support and Gaze Behaviors in Mixed Groups of Humans and Robots”. https://ieeexplore.ieee.org/document/9473499

Magic iCub: A Humanoid Robot Autonomously Catching Your Lies in a Card Game

Summary:

The article tries to create a robot that is autonomous, and is not driven with a Wizard of Oz approach. They try to make it autonomous by making the robots decisions based on the robot trying to measure a human’s inner state, they do this with eye tracking. Another goals of the robot is to do an entertaining activity with a human. For the game, the robot tries to guess which card is the secret card out of the 6 cards the human is describing. The human will pick 6 random cards, and pick 1 random card of the 6 to lie about when describing the cards. The card the human is lying about is the ‘secret card’. The cards don’t have any QR-codes, to reassure the human players that the robot could not cheat.

One of the problems that the robot has, is that the robot is sensitive to light-level changes. Although this is mainly an issue for outdoors, as light changes should not happen indoors.

In their conclusion they state that the robot is successfully able to measure a human’s inner state, as the robot has a high accuracy for guessing the correct secret card.  And that the robot is able to autonomously guide a human-robot interaction, as the measures of fun confirm that the game is entertaining.

Reference:

D. Pasquali, J. Gonzalez-Billandon, F. Rea, G. Sandini, A. Alessandra Sciutti (2021) . “Magic iCub: A Humanoid Robot Autonomously Catching Your Lies in a Card Game”. https://doi.org/10.1145/3434073.3444682

Just follow the suit! Trust in Human-Robot Interactions during Card Game Playing

Summary:

The paper’s aim was to create a social robot and an entertaining activity to reconnect the elderly, who often suffer from social isolation. They also hoped that it would contribute to the QoL elderly experience.

For their project they chose one of the most played games among elderly in Portugal, namely Sueca. Since robots become more competent, humans might start seeing them as fierce competitors. But people might still be ware of robots. Therefore, the paper tries to figure out how trust levels could work between humans and robots.

The authors concluded that humans do trust robots. But the level of trust they have in the robot, depends on previous encounters with the robot. Therefore, suggesting that to increase the trust level there has to be a longer period of being acquainted.

Reference:  

F. Correia, P. Alves-Oliveira, N. Maia, T. Ribeiro, S. Petisca, F. S. Melo, A. Paiva (2016). “Just follow the suit! Trust in Human-Robot Interactions during Card Game Playing”. 10.1109/ROMAN.2016.7745165

Motivational Factors for Mobile Serious Games for Elderly Users

Summary:

This paper focuses on understanding what motivates elderly users to use (serious) games. Serious games refer to game whose goal is not just amusement, so for example games that have also goal teaching the player something new.  The primary reason, users use a game is the usability of it. So games have to be adjusted to the needs of the elderly, such that it is easy to use. Elderly can namely have trouble with sight, hearing, attention, motor skills and using technology. Other motivations to use a game are that it is fun, relaxing, social interaction, gives regularly motivational messages, a good balance between ability and difficulty, and personalization of levels and time. Elderly also play games as a way to escape reality.

Reference:

R. N. S. de Carvalho, L. Ishitani (2012) “Motivational Factors for Mobile Serious Games for Elderly Users”. https://www.researchgate.net/publication/277709317_Motivational_Factors_for_Mobile_Serious_Games_for_Elderly_Users

Designing and Evaluating the Tabletop Game Experience for Senior Citizens

al Mahmud, A., Mubin, O., Shahid, S., & Martens, J.-B. (2008). Designing and Evaluating the Tabletop Game Experience for Senior Citizens. https://dl.acm.org/doi/pdf/10.1145/1463160.1463205

A Single-User Tabletop Card Game System for Older Persons: General Lessons Learned From an In-Situ Study

Gabrielli, S., Bellutti, S., Jameson, A., Leonardi, C., & Zancanaro, M. (2008). A single-user tabletop card game system for older persons: General lessons learned from an in-situ study. 2008 IEEE International Workshop on Horizontal Interactive Human Computer System, TABLETOP 2008, 85–88. https://doi.org/10.1109/TABLETOP.2008.4660188

Members

• Abel Brasse (1509128) - a.m.brasse@student.tue.nl
• Linda Geraets (1565834) - l.j.m.geraets@student.tue.nl
• Sander van der Leek (1564226) - s.j.m.v.d.leek@student.tue.nl
• Tom van Liempd (1544098) - t.g.c.v.liempd@student.tue.nl
• Luke van Dongen (1535242) - l.h.m.v.dongen@student.tue.nl
• Tom van Eemeren (1755595) - t.v.eemeren@student.tue.nl

Logbook

Week 1	Description of work done	Total time
Abel Brasse	meeting time (3h). Literature search, searching, reading, summarizing papers (3), User Study (2h)
Linda Geraets	Meeting monday (2h), Meeting wednesday (1h), Researching and reading papers (3h 30min), Summarizing papers (3h 30min), User Study (2h)	12h
Sander van der Leek
Tom van Liempd
Luke van Dongen
Tom van Eemeren	Course introduction(1h), Brainsorming (1h), Prepared agenda (30min), group meeting (1h), literature search (1h), reading through the literature (4h), summarising literature(1h)	9.5h