By Christopher J. Headleand, William J. Teahan, Llyr Ap Cenydd

This ebook constitutes the refereed court cases of the 1st overseas Symposium on synthetic lifestyles and clever brokers, ALIA 2014, held in Bangor, united kingdom, in November 2014. the ten revised complete papers have been conscientiously reviewed and chosen from 20 submissions. The papers are equipped in topical sections on studying and evolution; human interplay; robot simulation.

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Additional resources for Artificial Life and Intelligent Agents: First International Symposium, ALIA 2014, Bangor, UK, November 5-6, 2014. Revised Selected Papers

Example text

5 ∗ F2 (t, c)) nT ∗ (nC − sF C) (6) (7) where F1 (t, c) and F2 (t, c) are the values of the two fitness components at step c of trial t, rank (t, c) is the ranking of the activation of the categorization corresponding to the correct letter or object (that is, from 0, meaning the most activated and 4, meaning the least activated), yrt,c is the activation of the output t is the set corresponding to the right letter or object at step c of trial t, yw of activations of the wrong letters or objects at step c of trial t, nOL is the number of letters or objects, nT is the number of trials, nC is the number of steps in a trial (that is, 100) and sF C is the time step in which we start to compute fitness (that is, 50).

For two-player games, the payoffs can be represented by a bi-matrix (A, B), that gives the payoff for the row player in A, and the column player in B, see Fig. 1 (left). The goal of each player is to decide which action to take, so as to maximise their expected payoff.

As a result, when the problem domain becomes more complex, the dimension of the feature vector input to the network increases, and therefore the benefits from this kind of system are soon outweighed by the computational cost. Consequently, categorization using active vision has been used for more simple vision problems and discrimination of very few stimuli. For instance in [1], an Active Vision System based Genetic Algorithm evolved Neural Network was used for categorizing five gray-scale italic letters.

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