{"content":[{"text":"To evaluate such robotic game player, we have consid- \n ered two central aspects for assessing when playing in the \n card game: (1) competitiveness between team opponents and \n (2) cooperation between team partners. Performance can be \n measured by the number of games won, and simulations \n made with the developed algorithm to test it against other ar- \n tiﬁcial players. But, most importantly, to assess how humans \n perceive the robot as a game player we conducted a user \n study with 60 participants and measured the human players’ \n trust levels in their partners (before and after playing). Con- \n cerning the competence of the robot, the results show that \n the robot is able to play competitively with human players, \n achieving a winning rate of 60%. We also compared the trust \n level on the robotic partner with the trust level on human \n partners. The results show that human players signiﬁcantly \n increased their trust in the robot as their game partners (in a \n similar way to the trust levels they perceive to have towards \n human partners).","bbox":{"left":0.088235,"top":0.069138,"width":0.389765,"height":0.250393}},{"text":"to expect a reasonable performance, considering the obvious \n similarities between these two trick-taking card games.","bbox":{"left":0.522062,"top":0.06915,"width":0.3897299999999999,"height":0.028995000000000007}},{"text":"Related Work \n Robots are being developed and introduced in social envi- \n ronments with humans as tools for assistive (Feil-Seifer and \n Matari´c 2005), educational (Castellano et al. 2013) and even \n entertainment purposes (Pereira, Prada, and Paiva 2012). In \n the scope of this work, we will focus on entertaining ac- \n tivities - namely playing the card game of Sueca - with an \n artiﬁcial robotic companion. \n The gaming experience with an artiﬁcial player is an en- \n gaging experience due to the agent’s sociability and embod- \n iment (Behrooz, Rich, and Sidner 2014), either virtual or \n physical. Therefore, game playing scenarios became more \n popular to analyse the inﬂuence of physically embodied \n agents on different aspects that might be related with the \n game experience or with the perception of the artiﬁcial \n player. For instance, physical embodiment can provide a \n more immersive user experience, an improved game feed- \n back and a more believable social interaction (Pereira et al. \n 2008). Additionally, other studies revealed that empathic be- \n haviour can positively affect how children perceive a robot \n in a game playing scenario (Leite et al. 2012). \n Henceforth, different playful scenarios have been devel- \n oped, in which robots and humans interact and play together. \n Sometimes humans can behave as opponents towards each \n other and towards the robot, like in the Risk game scenario \n (Pereira, Prada, and Paiva 2012). In this game, the robot be- \n haves in the game as a socially aware agent that can play and \n socially interact with the human players. Its social behaviour \n is driven by its ability to have memory of the game actions \n of other players, be emotionally aware and consider social \n roles in the game (e.g., as Dominant or Exhibitionist towards \n the other players). In a more recent scenario, a robot played \n the Mastermind game with elderly people as a mean to pro- \n vide social support in a form of entertainment (Johnson et \n al. 2016). Although participants have indeed recognised the \n robot to have an entertaining associated value, the hypothe- \n sis that participants would enjoy more a robot displaying be- \n havioural patterns associated with the game progress when \n compared to randomly displayed behaviours was not sup- \n ported. \n Overall, the mentioned research studies prove the broad \n application of entertainment robotics among different age \n groups, at the same time they constitute insightful consid- \n erations for the design and development of any social en- \n tertainment robot. We aim to extend this line of research, \n by developing a scenario in which a robot plays either as \n an opponent and as a partner in the traditional card game of \n Sueca. This card game is a well-known game in Portugal and \n (young) adults and elderly play usually the game in social \n contexts of interaction, providing joyful moments together.","bbox":{"left":0.522059,"top":0.110471,"width":0.389772,"height":0.702477}},{"text":"Background \n PIMC is a search algorithm suitable for partially observable \n environments. It uses a Monte-Carlo methodology and deals \n with the imperfect information by the determinization tech- \n nique. In other words, the hidden information is sampled \n several times and the best move is computed by solving ex- \n actly or heuristically in each perfect information game. \n The main two disadvantages of this approach were \n pointed by Frank and Basin in 1998 (Frank and Basin 1998), \n and were called strategy fusion and non-locality. The ﬁrst \n one refers to decisions that would only make sense for cer- \n tain sampled distributions and when applied turned out to be \n poor decisions. The non-locality results from the fact that the \n value of a game tree node only considers its children values, \n however, in an imperfect information game, some guesses \n might be done using values of the non-local sub-tree. \n Nonetheless, this algorithm already counts with some suc- \n cessful implementations as the Ginsberg’s Intelligent Bridge \n player (GIB) (Ginsberg 2001), Skat (Buro et al. 2009), or \n Hearts (Sturtevant 2008). Their remarkable results seem to \n reveal the effectiveness of determinization. However, there \n were still difﬁculties in understanding the strong results of \n this algorithm. As such, Long et al. (Long et al. 2010) not \n only have analysed the previously mentioned problems of \n PIMC search, but the authors have also shown how three \n different properties of a game can inﬂuence the success of \n PIMC. The ﬁrst property is leaf correlation, which refers to \n how likely it is to affect a the payoff of a player in the neigh- \n bourhood of a leaf. When the probability of all siblings hav- \n ing the same payoff values is higher, the correlation value \n increases. Secondly, bias indicates the chance of a player \n being preferred over another. Finally, the last game charac- \n teristic that has been pointed is disambiguation factor, that \n denotes how rapidly the hidden information is revealed. The \n authors analysed these properties for the game of Skat, indi- \n cating a considerably good performance of PIMC, due to its \n values of leaf correlation, bias, and disambiguation factor \n (Long et al. 2010). This analysis strongly led us to consider \n PIMC in our ﬁrst implementation for the Sueca domain, and","bbox":{"left":0.088235,"top":0.335104,"width":0.389754,"height":0.554807}},{"text":"The Sueca card game \n Sueca is a Portuguese trick-taking card game played by four \n players divided into two teams. It uses a French deck with \n the cards 2-6, Q, J, K, 7 and A valuing 0, 2, 3, 4, 10 and","bbox":{"left":0.522059,"top":0.825204,"width":0.3897369999999999,"height":0.06469399999999992}},{"text":"24","bbox":{"left":0.492941,"top":0.934523,"width":0.01411800000000002,"height":0.015329999999999955}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}