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This paper presents a novel data-driven Topic Switch Model based on a cognitive representation of a limited set of topics that are currently in-focus, which determines what utterances are chosen next. The transition model was statistically learned from a large set of transcribed dyadic interactions. Results show that using our proposed model results in interactions that on average last 2.17 times longer compared to the same system without our model.

This paper presents the findings of an empirical study that explores player game experience by implementing the ERiSA Framework in games. A study with Action Role-Playing Game (RPG) was designed to evaluate player interactions with game companions, who were imbued with social and emotional skill by the ERiSA Framework. Players had to complete a quest in the Skyrim game, in which players had to use social and emotional skills to obtain a sword. The results clearly show that game companions who are capable of perceiving and exhibit emotions, are perceived to have personality and can forge relationships with the players, enhancing the player experience during the game.

This paper describes the evaluation of our fully integrated virtual game companions framework (ERiSA) [4]. We conducted three user studies with different scenarios using two versions of The Smile Game[4] in semi-public and public spaces. In our study, we show that the game companions' personality was successfully perceived by the participants while interacting and playing with the game companions. Topic about the game itself was the most popular topic with total 598 occurrences in our studies. Moreover, facial expressions is the most performed type of attack in the game. Finally, from the large number of video data collected, we aim to automatically learn the interaction rules and additional attack movements.

This paper presents a novel data-driven Topic Switch Model based on a cognitive representation of a limited set of topics that are currently in-focus, which determines what utterances are chosen next. The transition model was statistically learned from a large set of transcribed dyadic interactions. Results show that using our proposed model results in interactions that on average last 2.17 times longer compared to the same system without our model.

This paper presents the findings of an empirical study that explores player game experience by implementing the ERiSA Framework in games. A study with Action Role-Playing Game (RPG) was designed to evaluate player interactions with game companions, who were imbued with social and emotional skill by the ERiSA Framework. Players had to complete a quest in the Skyrim game, in which players had to use social and emotional skills to obtain a sword. The results clearly show that game companions who are capable of perceiving and exhibit emotions, are perceived to have personality and can forge relationships with the players, enhancing the player experience during the game.

This paper describes the evaluation of our fully integrated virtual game companions framework (ERiSA) [4]. We conducted three user studies with different scenarios using two versions of The Smile Game[4] in semi-public and public spaces. In our study, we show that the game companions' personality was successfully perceived by the participants while interacting and playing with the game companions. Topic about the game itself was the most popular topic with total 598 occurrences in our studies. Moreover, facial expressions is the most performed type of attack in the game. Finally, from the large number of video data collected, we aim to automatically learn the interaction rules and additional attack movements.

We propose an integrated framework for social and emotional game-agents to enhance their believability and quality of interaction, in particular by allowing an agent to forge social relations and make appropriate use of social signals. The framework is modular including sensing, interpretation, behaviour generation, and game components. We propose a generic formulation of action selection rules based on observed social and emotional signals, the agent’s personality, and the social relation between agent and player. The rules are formulated such that its variables can easily be obtained from real data. We illustrate and evaluate our framework using a simple social game called The Smile Game.

We present a novel computational model of emotion, personality and social relationships, implemented and evaluated in an existing commercial game (The Elder Scrolls V: Skyrim). We argue that Non Player Characters (NPCs) with such capabilities will accommodate a new experience in playing games and provide evidence supporting this. Applying the ERiSA Framework [1, 2] to the Skyrim Creation Kit, we designed a simple quest and 2 unique NPCs to interact with. When the ERiSA framework is used, players reported significant changes in their social relationship with the two NPCs compared to the baseline. Most importantly, the results further indicate that the models provide a new experience in playing games. In particular, players report enhanced emotional attachment to the NPCs and appear to forge relationships with the NPCs. Finally, the implemented models result in significant changes in the game engagement and the game immersion score.

This paper presents a novel data-driven Topic Switch Model based on a cognitive representation of a limited set of topics that are currently in-focus, which determines what utterances are chosen next. The transition model was statistically learned from a large set of transcribed dyadic interactions. Results show that using our proposed model results in interactions that on average last 2.17 times longer compared to the same system without our model.

This paper presents the findings of an empirical study that explores player game experience by implementing the ERiSA Framework in games. A study with Action Role-Playing Game (RPG) was designed to evaluate player interactions with game companions, who were imbued with social and emotional skill by the ERiSA Framework. Players had to complete a quest in the Skyrim game, in which players had to use social and emotional skills to obtain a sword. The results clearly show that game companions who are capable of perceiving and exhibit emotions, are perceived to have personality and can forge relationships with the players, enhancing the player experience during the game.

We present a novel computational model of emotion, personality and social relationships, implemented and evaluated in an existing commercial game (The Elder Scrolls V: Skyrim). We argue that Non Player Characters (NPCs) with such capabilities will accommodate a new experience in playing games and provide evidence supporting this. Applying the ERiSA Framework [1, 2] to the Skyrim Creation Kit, we designed a simple quest and 2 unique NPCs to interact with. When the ERiSA framework is used, players reported significant changes in their social relationship with the two NPCs compared to the baseline. Most importantly, the results further indicate that the models provide a new experience in playing games. In particular, players report enhanced emotional attachment to the NPCs and appear to forge relationships with the NPCs. Finally, the implemented models result in significant changes in the game engagement and the game immersion score.

We propose an integrated framework for social and emotional game-agents to enhance their believability and quality of interaction, in particular by allowing an agent to forge social relations and make appropriate use of social signals. The framework is modular including sensing, interpretation, behaviour generation, and game components. We propose a generic formulation of action selection rules based on observed social and emotional signals, the agent’s personality, and the social relation between agent and player. The rules are formulated such that its variables can easily be obtained from real data. We illustrate and evaluate our framework using a simple social game called The Smile Game.

This thesis presents a fully integrated and modular framework for social and emotional game companion that focuses on realistic social interaction between the player and Non-Player Characters (NPCs) in a game environment. Moreover, this thesis proposes integrated computational models for a formulation of action selection rules based on the game companion’s personality, the relation between player and game companion and the perceived (non-)verbal actions to enhance the NPC’s believability and the player’s interaction in a game context. The rules were derived from data collections of both human-human and human-machine interactions. This thesis argues that NPC with such capability will accommodate a new experience when playing games. To illustrates the effectiveness of the proposed framework and computational models, the author implemented the framework and computational models into two game scenarios: The Smile Game where a human player who abides in the real world played with an ECA who inhabits a virtual world. The second scenario was The Skyrim Game, where an avatar represented the human player, who played with a virtual NPC in a virtual world. With a total of 117 participants in 217 interactions with the system, the results show that the players evidently perceived the NPCs’ personality in accordance with the one set to them. The NPCs’ ability to display emotions appropriately also provides the feeling of immersion in games to the players. Moreover, the ability to forge relationships naturally with the game companions gives the sentimental feeling towards the game companions. The main contribution of the work presented to the field of Intelligence Virtual Agents specifically in the domain of computer games, in this thesis is A fully integrated and modular framework for social-affect-aware game design complete with the computational models for social interaction with game companions. Several games have been built in several scenarios with simple social interactions

This paper describes the evaluation of our fully integrated virtual game companions framework (ERiSA) [4]. We conducted three user studies with different scenarios using two versions of The Smile Game[4] in semi-public and public spaces. In our study, we show that the game companions' personality was successfully perceived by the participants while interacting and playing with the game companions. Topic about the game itself was the most popular topic with total 598 occurrences in our studies. Moreover, facial expressions is the most performed type of attack in the game. Finally, from the large number of video data collected, we aim to automatically learn the interaction rules and additional attack movements.

We propose an integrated framework for social and emotional game-agents to enhance their believability and quality of interaction, in particular by allowing an agent to forge social relations and make appropriate use of social signals. The framework is modular including sensing, interpretation, behaviour generation, and game components. We propose a generic formulation of action selection rules based on observed social and emotional signals, the agent’s personality, and the social relation between agent and player. The rules are formulated such that its variables can easily be obtained from real data. We illustrate and evaluate our framework using a simple social game called The Smile Game.

This paper presents the findings of an empirical study that explores player game experience by implementing the ERiSA Framework in games. A study with Action Role-Playing Game (RPG) was designed to evaluate player interactions with game companions, who were imbued with social and emotional skill by the ERiSA Framework. Players had to complete a quest in the Skyrim game, in which players had to use social and emotional skills to obtain a sword. The results clearly show that game companions who are capable of perceiving and exhibit emotions, are perceived to have personality and can forge relationships with the players, enhancing the player experience during the game.

This paper presents a novel data-driven Topic Switch Model based on a cognitive representation of a limited set of topics that are currently in-focus, which determines what utterances are chosen next. The transition model was statistically learned from a large set of transcribed dyadic interactions. Results show that using our proposed model results in interactions that on average last 2.17 times longer compared to the same system without our model.

We present a novel computational model of emotion, personality and social relationships, implemented and evaluated in an existing commercial game (The Elder Scrolls V: Skyrim). We argue that Non Player Characters (NPCs) with such capabilities will accommodate a new experience in playing games and provide evidence supporting this. Applying the ERiSA Framework [1, 2] to the Skyrim Creation Kit, we designed a simple quest and 2 unique NPCs to interact with. When the ERiSA framework is used, players reported significant changes in their social relationship with the two NPCs compared to the baseline. Most importantly, the results further indicate that the models provide a new experience in playing games. In particular, players report enhanced emotional attachment to the NPCs and appear to forge relationships with the NPCs. Finally, the implemented models result in significant changes in the game engagement and the game immersion score.

This paper describes the evaluation of our fully integrated virtual game companions framework (ERiSA) [4]. We conducted three user studies with different scenarios using two versions of The Smile Game[4] in semi-public and public spaces. In our study, we show that the game companions' personality was successfully perceived by the participants while interacting and playing with the game companions. Topic about the game itself was the most popular topic with total 598 occurrences in our studies. Moreover, facial expressions is the most performed type of attack in the game. Finally, from the large number of video data collected, we aim to automatically learn the interaction rules and additional attack movements.