The Role of Implicit Motives in Strategic Decision-Making: Computational Models of Motivated Learning and the Evolution of Motivated Agents

Kasmarik, Kathryn

doi:10.3390/g6040604

Cited by 7 publications

(4 citation statements)

References 37 publications

(49 reference statements)

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“…In summary, player modeling and simulation-based game testing using intrinsically motivated agents appears to still be a young field dominated by a few prolific authors such as Merrick [53,51,55,52,54].…”

Section: Summary and Discussionmentioning

confidence: 99%

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Review of Intrinsic Motivation in Simulation-based Game Testing

Roohi

Takatalo

Guckelsberger

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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This paper presents a review of intrinsic motivation in player modeling, with a focus on simulation-based game testing. Modern AI agents can learn to win many games; from a game testing perspective, a remaining research problem is how to model the aspects of human player behavior not explained by purely rational and goal-driven decision making. A major piece of this puzzle is constituted by intrinsic motivations, i.e., psychological needs that drive behavior without extrinsic reinforcement such as game score. We first review the common intrinsic motivations discussed in player psychology research and artificial intelligence, and then proceed to systematically review how the various motivations have been implemented in simulated player agents. Our work reveals that although motivations such as competence and curiosity have been studied in AI, work on utilizing them in simulation-based game testing is sparse, and other motivations such as social relatedness, immersion, and domination appear particularly underexplored.

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Section: Summary and Discussionmentioning

confidence: 99%

“…In a continuation study, Merrick [52] used the same computational model of motivation in order to highlight the role of motivation in decision making. They demonstrated how different behaviors of agents emerged in a game theoretic setting by different parameters of the computational model.…”

Section: Competence and Autonomymentioning

confidence: 99%

Review of Intrinsic Motivation in Simulation-based Game Testing

Roohi

Takatalo

Guckelsberger

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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show abstract

“…Merrick ( 2015 ) demonstrated the effects of motive profiles in game-playing agents. With the help of two-player game settings, it was shown that power, achievement, and affiliation motives can lead to various emergent behaviors.…”

Section: Survey: From Motivation In Individual Agents To Motivation Imentioning

confidence: 99%

Toward Computational Motivation for Multi-Agent Systems and Swarms

2018

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Motivation is a crucial part of animal and human mental development, fostering competence, autonomy, and open-ended development. Motivational constructs have proved to be an integral part of explaining human and animal behavior. Computer scientists have proposed various computational models of motivation for artificial agents, with the aim of building artificial agents capable of autonomous goal generation. Multi-agent systems and swarm intelligence are natural extensions to the individual agent setting. However, there are only a few works that focus on motivation theories in multi-agent or swarm settings. In this study, we review current computational models of motivation settings, mechanisms, functions and evaluation methods and discuss how we can produce systems with new kinds of functions not possible using individual agents. We describe in detail this open area of research and the major research challenges it holds.

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“…Enduring motive dispositions or ‘implicit motives’ [ 44 ] are preferences for certain types of incentive acquired in early childhood [ 45 ]. Incentives are the situational characteristics associated with the possible satisfaction of a motive.…”

Section: Trusted Autonomymentioning

confidence: 99%

Trusted Autonomy and Cognitive Cyber Symbiosis: Open Challenges

et al. 2015

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This paper considers two emerging interdisciplinary, but related topics that are likely to create tipping points in advancing the engineering and science areas. Trusted Autonomy (TA) is a field of research that focuses on understanding and designing the interaction space between two entities each of which exhibits a level of autonomy. These entities can be humans, machines, or a mix of the two. Cognitive Cyber Symbiosis (CoCyS) is a cloud that uses humans and machines for decision-making. In CoCyS, human-machine teams are viewed as a network with each node comprising humans (as computational machines) or computers. CoCyS focuses on the architecture and interface of a Trusted Autonomous System. This paper examines these two concepts and seeks to remove ambiguity by introducing formal definitions for these concepts. It then discusses open challenges for TA and CoCyS, that is, whether a team made of humans and machines can work in fluid, seamless harmony.

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The Role of Implicit Motives in Strategic Decision-Making: Computational Models of Motivated Learning and the Evolution of Motivated Agents

Cited by 7 publications

References 37 publications

Review of Intrinsic Motivation in Simulation-based Game Testing

Review of Intrinsic Motivation in Simulation-based Game Testing

Toward Computational Motivation for Multi-Agent Systems and Swarms

Trusted Autonomy and Cognitive Cyber Symbiosis: Open Challenges

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