<div>Cognitive agent architectures embed social learning algorithms and normative frameworks for adopting others’ influenced goals. However, there exists inefficiency in providing continuous, situational decision-making to emerge social, altruistic norms. The thesis reconstructs social-ecological learning mechanisms to functionally and efficiently internalize situational cooperation. By orienting agents to be self-aware of their three-dimensional vectors, i.e., physical, emotional and intellectual in graphical representations, this thesis hypothesizes the parsimonious, action-predictive four emotions that not only link perceptions, action, and cognition by events but also the emotional continuity functional to social-ecological rationality of agents in continuum. Twelve Meridian system is employed to conceptualize the equalizing, complementary, heuristic orientation (ECHO) model. ECHO simulates “naturalistic” cooperation to model embodied, social-ecological orientations by self-organizing emotions to emerge functional social network formations. ECHO delineates the soma links to perceptions, namely Twelve Meridian channels as “direct pipes” that initiates and conduct emotions and consciousness of three dimensional agenthood: physical, emotional, and intellectual desires. ECHO reconstructs emotions as entities to induce systemic, self-organized rule of delegation by integrating agents’ percepts and actuations. By modeling constitutional emotions and consciousness of eight entities, emotions within entities as “individualized emotional processors,” are constructing and integrating purposeful social, altruistic events for the efficacy of situated agents.<br></div>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/7435568 |
| Date | 03 January 2019 |
| Creators | Eunsun C. Smith (5930864) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Equalizing_Complementary_Heuristic_Orientation_of_Situated_Agents/7435568 |
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