{'fraction_non_alphanumeric': 0.013215859030837005, 'fraction_numerical': 0.0, 'mean_word_length': 5.616504854368932, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Implementing a Theory of Attachment : A Simulation of the Strange Situation with Autonomous Agents

This paper presents a number of autonomous agent simulations as theories of infant attachment. In particular, the simulations model the behaviour of one year old human infants observed in Strange Situation studies. A key question this study is attempting to answer is, for a normal population of infants, why do responses tend to cluster into three groups of similar response? In addition this work explores what architectural mechanisms underlie the ability to switch goals, learn different predispositions to hold goals and learn about the effectiveness of different actions. Simulations are described that provide explanations of these phenomena. Computational experiments were carried out to investigate the relative effects of two learning mechanisms. Firstly, infants learned about their attachment relationships solely from carer responses in episodes when they experience anxiety. Secondly, infants learned solely from episodes where they experience warmth or rejection in a non-anxious environment. Results show that learning from episodes where anxiety was experienced causes greater clustering of responses. This result explains the clustering phenomena at a high level of explanation that is independent of any low level physiological implementation.