Artificial Agents
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Mechanism design (MD). Parkes [146] described mechanism design as a
problem for designing a protocol, distributing and implementing particular objectives of self-interested individual agents. An agent makes a decision respecting other agents, based on its own private information and
behaves selfishly. The Economics Nobel Prize for 2007 was presented to
the Mechanism Design Theory [93]. It follows the “Hayek theory of catallaxy where ‘self-organizing system of voluntary co-operation’ is brought
about as market progress”. However, there is criticism to the theory,
stating that if MD were used to design markets, some agents still end
up monopolizing markets.
Gaussian adaptation. Evolutionary algorithms designed for stochastic
adaptive processes take more than one attribute into consideration. The
number of samples is denoted by N dimensional vectors to represent
multivariate Gaussian distribution.
Learning classifier systems. These LCS use reinforcement learning and
genetic algorithms. The rules can be updated using reinforcement learning, allowing different strategies to be chosen.
• Pittsburgh-type LCS - population of separate rule set represented
by GA, recombines and produces best of rule sets.
• Michigan-style LCS - focuses on choosing best within a given rule
set.
Reinforcement learning. As described above for optimizing behavior.
Self-organizing map. Similar to Kohonen map, it uses unsupervised learning to produce low-dimensional representation of training samples, while
keeping the topological properties of input space. Uses a feed forward
network structure with weights to choose neurons and produce Gaussian
functions.
Memetic algorithms. Learning algorithms, a combination of swarm optimization and genetic algorithms. Each individual program is chosen from
a population and allowed to evolve. Each individual uses a learning technique to evolve either Lamarckian or Baldwinian learning. Lamarckain
[113] theories use environments to change individuals, known as the
adaptive force. Baldwinian [18] uses learning in genetic material of the
individual. These are supported by trial-and-error and social learning
theories. For instance, trait becomes stronger as a consequence of interaction with the environment. Individuals who learn quickly are at
an advantage. Blackmore distinguishes the difference between these two
modes of inheritance in the evolution of memes, characterising the Darwinian mode as ‘copying the instructions’ and the Lamarckian as ‘copying the product’ [24]. Each program is treated as a meme. The next step
involves these memes to coevolve to fit the problem domain.
41
Mechanism design (MD). Parkes [146] described mechanism design as a
problem for designing a protocol, distributing and implementing particular objectives of self-interested individual agents. An agent makes a decision respecting other agents, based on its own private information and
behaves selfishly. The Economics Nobel Prize for 2007 was presented to
the Mechanism Design Theory [93]. It follows the “Hayek theory of catallaxy where ‘self-organizing system of voluntary co-operation’ is brought
about as market progress”. However, there is criticism to the theory,
stating that if MD were used to design markets, some agents still end
up monopolizing markets.
Gaussian adaptation. Evolutionary algorithms designed for stochastic
adaptive processes take more than one attribute into consideration. The
number of samples is denoted by N dimensional vectors to represent
multivariate Gaussian distribution.
Learning classifier systems. These LCS use reinforcement learning and
genetic algorithms. The rules can be updated using reinforcement learning, allowing different strategies to be chosen.
• Pittsburgh-type LCS - population of separate rule set represented
by GA, recombines and produces best of rule sets.
• Michigan-style LCS - focuses on choosing best within a given rule
set.
Reinforcement learning. As described above for optimizing behavior.
Self-organizing map. Similar to Kohonen map, it uses unsupervised learning to produce low-dimensional representation of training samples, while
keeping the topological properties of input space. Uses a feed forward
network structure with weights to choose neurons and produce Gaussian
functions.
Memetic algorithms. Learning algorithms, a combination of swarm optimization and genetic algorithms. Each individual program is chosen from
a population and allowed to evolve. Each individual uses a learning technique to evolve either Lamarckian or Baldwinian learning. Lamarckain
[113] theories use environments to change individuals, known as the
adaptive force. Baldwinian [18] uses learning in genetic material of the
individual. These are supported by trial-and-error and social learning
theories. For instance, trait becomes stronger as a consequence of interaction with the environment. Individuals who learn quickly are at
an advantage. Blackmore distinguishes the difference between these two
modes of inheritance in the evolution of memes, characterising the Darwinian mode as ‘copying the instructions’ and the Lamarckian as ‘copying the product’ [24]. Each program is treated as a meme. The next step
involves these memes to coevolve to fit the problem domain.