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A Social Robot As a Card Game Player

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A Social Robot As a Card Game Player Proceedings, The Thirteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-17) A Social Robot as a Card Game Player Filipa Correia,1 Patr´ıcia Alves-Oliveira,2 Tiago Ribeiro,1 Francisco S. Melo,1 Ana Paiva1 1INESC-ID, Lisbon, Portugal and Instituto Superior Tecnico,´ Universidade de Lisboa, Lisbon, Portugal 2Instituto Universitario´ de Lisboa (ISCTE-IUL), Lisbon, Portugal; CIS-IUL, Lisbon, Portugal; and INESC-ID, Lisbon, Portugal Abstract behaviours, but they also hinder performance aspects related with the competitive nature of the game itself. This paper describes a social robotic game player that is able to successfully play a team card game called Sueca. The ques- Furthermore, in many types of games, current advances in tion we will address in this paper is: how can we build a social Artificial Intelligence (AI) over the past few years has shown robot player that is able to balance its ability to play the card that strong and powerful algorithms combined with signifi- game with natural and social behaviours towards its partner cant amounts of data, are able to defeat human world cham- and its opponents. The first challenge we faced concerned the pions of these games (see for example, the game of Go). development of a competent artificial player for a hidden in- These results raise our expectations and people are starting formation game, whose time constraint is the average human to consider such artificial agents as fierce competitors. Yet, decision time. To accomplish this requirement, the Perfect In- when we consider multi-player games, where the social en- formation Monte-Carlo (PIMC) algorithm was used. Further, vironment becomes more relevant, and when the games are we have performed an analysis of this algorithm’s possible parametrizations for games trees that cannot be fully explored played in the physical world, how will people perceive a so- in a reasonable amount of time with a MinMax search. Ad- cial robotic player compared to human standards? Will peo- ditionally, given the nature of the Sueca game, such robotic ple be willing to trust a social robot to be his partner in a player must master the social interactions both as a partner team game? and as an opponent. To do that, an emotional agent frame- To address these questions we created a social au- work (FAtiMA) was used to build the emotional and social tonomous robotic partner for the Sueca card game, with a behaviours of the robot. At each moment, the robot not only twofold goal of both playing competitively and interacting plays competitively but also appraises the situation and re- socially with the other players. The development of such sponds emotionally in a natural manner. To test the approach, robotic game player introduces some challenging aspects, in we conducted a user study and compared the levels of trust participants attributed to the robots and to human partners. particular finding the best balance between social responses Results have shown that the robot team exhibited a winning and computations related with the game, in order for the so- rate of 60%. Concerning the social aspects, the results also cially intelligent agent to produce natural and human-like showed that human players increased their trust in the robot behaviours. as their game partners (similar to the way to the trust levels Another important challenge of creating an intelligent change towards human partners). agent in this social context is the time constraint on the com- putation of a hidden information card game. State-of-the-art As interactive entertainment expands, computer games approaches, for instance PIMC, promise good results on the are progressively moving from the virtual world back to the Sueca domain according to the game properties. However, physical world. Augmented reality games, haptic interfaces the full computation of multiple perfect information games in gaming, touch tables, etc, are some of the types of in- is not time-efficient and will hinder natural interaction in teractivity placing human players in physically situated en- a game with human players. Therefore, this paper also ex- tertainment experiences. In parallel with this move into the plores how the algorithm’s parametrizations affect the game physical world, artificial partners and opponents can also be results in order to choose the best performance-time config- created to exist in such physical world. To do that, the area uration. of entertainment robots offers challenging opportunities as Finally, by using an expressive robot that is able to ex- it explores the role of a robot as a game player. In general, press emotions, provide spoken feedback, and respond so- social robots can contribute with new and broad ways of cre- cially, the game experience can be created balancing these ating socially engaging interactions with humans in enter- social and game competencies. In this case, we built the so- tainment contexts. The challenges of these human-robot in- cial competencies by using an emotional agent framework teractions may vary from game to game. Some games, when (FatiMA) which allows for emotional appraisal to occur and played in the physical world not only hold complex social fire social and emotional behaviours. At each moment, the Copyright c 2017, Association for the Advancement of Artificial robot not only plays competitively, but also appraises the sit- Intelligence (www.aaai.org). All rights reserved. uation and responds emotionally to the game situations. 23 To evaluate such robotic game player, we have consid- to expect a reasonable performance, considering the obvious ered two central aspects for assessing when playing in the similarities between these two trick-taking card games. card game: (1) competitiveness between team opponents and (2) cooperation between team partners. Performance can be Related Work measured by the number of games won, and simulations made with the developed algorithm to test it against other ar- Robots are being developed and introduced in social envi- tificial players. But, most importantly, to assess how humans ronments with humans as tools for assistive (Feil-Seifer and perceive the robot as a game player we conducted a user Mataric´ 2005), educational (Castellano et al. 2013) and even study with 60 participants and measured the human players’ entertainment purposes (Pereira, Prada, and Paiva 2012). In trust levels in their partners (before and after playing). Con- the scope of this work, we will focus on entertaining ac- cerning the competence of the robot, the results show that tivities - namely playing the card game of Sueca - with an the robot is able to play competitively with human players, artificial robotic companion. achieving a winning rate of 60%. We also compared the trust The gaming experience with an artificial player is an en- level on the robotic partner with the trust level on human gaging experience due to the agent’s sociability and embod- partners. The results show that human players significantly iment (Behrooz, Rich, and Sidner 2014), either virtual or increased their trust in the robot as their game partners (in a physical. Therefore, game playing scenarios became more similar way to the trust levels they perceive to have towards popular to analyse the influence of physically embodied human partners). agents on different aspects that might be related with the game experience or with the perception of the artificial player. For instance, physical embodiment can provide a Background more immersive user experience, an improved game feed- PIMC is a search algorithm suitable for partially observable back and a more believable social interaction (Pereira et al. environments. It uses a Monte-Carlo methodology and deals 2008). Additionally, other studies revealed that empathic be- with the imperfect information by the determinization tech- haviour can positively affect how children perceive a robot nique. In other words, the hidden information is sampled in a game playing scenario (Leite et al. 2012). several times and the best move is computed by solving ex- Henceforth, different playful scenarios have been devel- actly or heuristically in each perfect information game. oped, in which robots and humans interact and play together. Sometimes humans can behave as opponents towards each The main two disadvantages of this approach were other and towards the robot, like in the Risk game scenario pointed by Frank and Basin in 1998 (Frank and Basin 1998), (Pereira, Prada, and Paiva 2012). In this game, the robot be- and were called strategy fusion and non-locality. The first haves in the game as a socially aware agent that can play and one refers to decisions that would only make sense for cer- socially interact with the human players. Its social behaviour tain sampled distributions and when applied turned out to be is driven by its ability to have memory of the game actions poor decisions. The non-locality results from the fact that the of other players, be emotionally aware and consider social value of a game tree node only considers its children values, roles in the game (e.g., as Dominant or Exhibitionist towards however, in an imperfect information game, some guesses the other players). In a more recent scenario, a robot played might be done using values of the non-local sub-tree. the Mastermind game with elderly people as a mean to pro- Nonetheless, this algorithm already counts with some suc- vide social support in a form of entertainment (Johnson et cessful implementations as the Ginsberg’s Intelligent Bridge al. 2016). Although participants have indeed recognised the player (GIB) (Ginsberg 2001), Skat (Buro et al. 2009), or robot to have an entertaining associated value, the hypothe- Hearts (Sturtevant 2008). Their remarkable results seem to sis that participants would enjoy more a robot displaying be- reveal the effectiveness of determinization. However, there havioural patterns associated with the game progress when were still difficulties in understanding the strong results of compared to randomly displayed behaviours was not sup- this algorithm.
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