Companion characters in are present in many video games across genres, serving the role of the player's partner. Their goal is to support the player's strategy and to immerse the player by providing a believable companion. These companions often only perform rigidly scripted actions and fail to adapt to an individual player's play-style, detracting from their usefulness. Behavior like this can also become frustrating to the player if the companions become more of a hindrance than they are a benefit. Other work, including this project's precursor, focused on building companions that mimic the player. These strategies customize the companion's actions to each player, but are limited. In the same context, an ideal companion would help further the player's strategy by finding complementary actions rather than blind emulation.
We propose a game-development framework that adds complementary (rather than mimicking) companions to a video game. For the purposes of this framework a \"complementary\" action is defined as any that furthers the player's strategy both in the immediate future as well as in the long-term. This is determined through a combination of both player-action and game-state prediction processes, while allowing the companion to experiment with actions the player hasn't tried. We used a new method to determine the location of companion actions based on a dynamic set of regions customized to the individual player. A user study of game-development students showed promising results, with a seventeen out of twenty-five participants reacting positively to the companion behavior, and nineteen saying that they would consider using the framework in future games.
","abstract_html":"<p>Companion characters in are present in many video games across genres, serving the role of the player's partner. Their goal is to support the player's strategy and to immerse the player by providing a believable companion. These companions often only perform rigidly scripted actions and fail to adapt to an individual player's play-style, detracting from their usefulness. Behavior like this can also become frustrating to the player if the companions become more of a hindrance than they are a benefit. Other work, including this project's precursor, focused on building companions that mimic the player. These strategies customize the companion's actions to each player, but are limited. In the same context, an ideal companion would help further the player's strategy by finding complementary actions rather than blind emulation.</p> <p>We propose a game-development framework that adds complementary (rather than mimicking) companions to a video game. For the purposes of this framework a "complementary" action is defined as any that furthers the player's strategy both in the immediate future as well as in the long-term. This is determined through a combination of both player-action and game-state prediction processes, while allowing the companion to experiment with actions the player hasn't tried. We used a new method to determine the location of companion actions based on a dynamic set of regions customized to the individual player. A user study of game-development students showed promising results, with a seventeen out of twenty-five participants reacting positively to the companion behavior, and nineteen saying that they would consider using the framework in future games.</p>","abstract_has_math":false,"creators":["Scott, Gavin"],"institution":null,"degree_name":"MS in Computer Science","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Foaad Khosmood"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-06-01T07:00:00Z","date_published":"2017-06-01T07:00:00Z","updated_at":"2026-07-24T01:31:49Z","subjects":["Artificial Intelligence","Video Game","Companion","Machine Learning","Complementary Behavior","Framework","Computational Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2017.55"],"render_values":[{"text":"10.15368/theses.2017.55","href":"https://doi.org/10.15368/theses.2017.55","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1744","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Foaad Khosmood"]},{"key":"dc:creator","label":"Author","values":["Scott, Gavin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2017-06-15T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Artificial Intelligence","Video Game","Companion","Machine Learning","Complementary Behavior","Framework","Computational Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1744","10.15368/theses.2017.55"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Companion characters in are present in many video games across genres, serving the role of the player's partner. Their goal is to support the player's strategy and to immerse the player by providing a believable companion. These companions often only perform rigidly scripted actions and fail to adapt to an individual player's play-style, detracting from their usefulness. Behavior like this can also become frustrating to the player if the companions become more of a hindrance than they are a benefit. Other work, including this project's precursor, focused on building companions that mimic the player. These strategies customize the companion's actions to each player, but are limited. In the same context, an ideal companion would help further the player's strategy by finding complementary actions rather than blind emulation.
We propose a game-development framework that adds complementary (rather than mimicking) companions to a video game. For the purposes of this framework a \"complementary\" action is defined as any that furthers the player's strategy both in the immediate future as well as in the long-term. This is determined through a combination of both player-action and game-state prediction processes, while allowing the companion to experiment with actions the player hasn't tried. We used a new method to determine the location of companion actions based on a dynamic set of regions customized to the individual player. A user study of game-development students showed promising results, with a seventeen out of twenty-five participants reacting positively to the companion behavior, and nineteen saying that they would consider using the framework in future games.
"]},{"key":"dc:title","label":"Title","values":["Complementary Companion Behavior in Video Games"]}]}],"canonical_facts":{"dc:contributor":["Foaad Khosmood"],"dc:creator":["Scott, Gavin"],"dc:date.available":["2017-06-15T07:00:00Z"],"dc:description.abstract":["Companion characters in are present in many video games across genres, serving the role of the player's partner. Their goal is to support the player's strategy and to immerse the player by providing a believable companion. These companions often only perform rigidly scripted actions and fail to adapt to an individual player's play-style, detracting from their usefulness. Behavior like this can also become frustrating to the player if the companions become more of a hindrance than they are a benefit. Other work, including this project's precursor, focused on building companions that mimic the player. These strategies customize the companion's actions to each player, but are limited. In the same context, an ideal companion would help further the player's strategy by finding complementary actions rather than blind emulation.
We propose a game-development framework that adds complementary (rather than mimicking) companions to a video game. For the purposes of this framework a \"complementary\" action is defined as any that furthers the player's strategy both in the immediate future as well as in the long-term. This is determined through a combination of both player-action and game-state prediction processes, while allowing the companion to experiment with actions the player hasn't tried. We used a new method to determine the location of companion actions based on a dynamic set of regions customized to the individual player. A user study of game-development students showed promising results, with a seventeen out of twenty-five participants reacting positively to the companion behavior, and nineteen saying that they would consider using the framework in future games.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1744","10.15368/theses.2017.55"],"dc:subject":["Artificial Intelligence","Video Game","Companion","Machine Learning","Complementary Behavior","Framework","Computational Engineering"],"dc:title":["Complementary Companion Behavior in Video Games"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:31:49Z"}