User:Kekasmarik/sandbox
Computational motivation models, simulates or replicates motivation using a computer, to achieve one of several ends:
- To construct a program or computer capable of exhibiting characteristics of human motivation.
- To better understand human motivation and to formulate an algorithmic perspective on self-motivated behaviour in humans.
Motivation is the "cause of action" in natural systems [1]. In artificial agents, such as robots or non-player characters in computer games, computational motivation causes action by focusing attention and generating goals from experiences.
Defining Motivation in Computational Terms
[edit]As measured by the amount of activity in the field
Computational Curiosity
[edit]Bla
Achievement Motivation
[edit]Boden also distinguishes between the creativity that arises
Affiliation Motivation
[edit]Boden also distinguishes between the creativity that arises
Power Motivation
[edit]Boden also distinguishes between the creativity that arises
Competence or Learning Progress Motivation
[edit]Boden also distinguishes between the creativity that arises
Computational Drives
[edit]Bla
Motivation in Artificial Intelligence
[edit]Computational motivation has been incorporated with a range of well-known artificial intelligence techniques.
Motivated Reinforcement Learning
[edit]Reinforcement learning agents [2] learn from trial-and-error and reward feedback. Agents experiment with different actions in each situation they encounter and received reward or punishment. They progressively map reward earned to the situations and/or actions that earn them, and favour these situations and/or actions over time. When static reward signals are crafted around a particular goal, reinforcement learning agents will learn behaviours that solve that goal. However, with dynamic reward signals that use principles such as curiosity, novelty or competence-seeking, motivated reinforcement learning agents [3][4] are created. Motivated reinforcement learning agents can focus their attention on different goals at different times. The agent designer no longer needs to know what goals the agent may encounter during its lifetime.
Motivated Particle Swarm Optimisation
[edit]Traditional particle swarm optimisation algorithms take a fitness function as input and compute successive changes to the velocities of a number of particles. Two classes of motivated particle swarm optimisation have been proposed. Algorithms in the first class employ motivation to generate a dynamic objective function as a function of spatially mapped sensor data, while optimisation is in progress [5]. Algorithms in the second class optimise a fixed fitness function, but employ computational motivation to embed diversity in the particles [6].
Motivated Crowds
[edit]Boden also distinguishes between the creativity that arises
Computational Motivation and Evolution
[edit]Boden also distinguishes between the creativity that arises
Computational Motivation in Game Theory
[edit]Bla
Motivation in Cognitive Architectures
[edit]Clarion Vernon's one
Applications of Computational Motivation
[edit]Before 1989, artificial neural networks have been used to model certain aspects of creativity.
Computer Games
[edit]bla
Design Evaluation
[edit]bla
Developmental Robotics
[edit]bla
See also
[edit]References
[edit]- ^ Heckhausen, J; Heckhausen, H (2010), Motivation and Action, Cambridge University Presss
- ^ Sutton, R; Barto, A (2000), Reinforcement Learning:An Introduction, The MIT Press
- ^ Merrick, K; Maher, ML (2009), Motivated Reinforcement Learning: Curious Characters for Multiuser Games, Springer Verlag
- ^ Singh, S; Barto, A; Chentanez, N (2005), Intrinsically motivated reinforcement learning, Advances in Neural Information Processing Systems 17 (NIPS), pp. 1281-1288.
- ^ Klyne, A; Merrick, K (2016), Intrinsically motivated particle swarm optimisation applied to task allocation for workplace hazard detection, Adaptive Behaviour vol. 24 no. 4 219-236
- ^ Hardhienata, M; Merrick, K; Ougrinovski, V (2012), Task allocation in multi-agent systems using models of motivation and leadership, IEEE Congress on Evolutionary Computation