This research investigates the concept of mind as a control system using the “Society of Agents” metaphor, whereby the whole is described as the collective behavior of simple and intelligent agents. This powerful concept for mind research benefits from the use of metacognition, and eases the development of a self configurable computational model. A six tiered SMCA (Society of Mind Cognitive Architecture) control model is designed that relies on a society of agents operating using metrics associated with the principles of artificial economics in animal cognition. Qualities such as level of decision making, its cost function and utility behavior (the microeconomic level), physiological and goal oriented behavior are investigated. The research builds on current work, and shows the use of affect norms as metacontrol heuristics enables the computational model to adapt and learn in order to optimize its behavior.
The research identifies the concepts of consciousness and commonsense. It also investigates and demonstrates how consciousness level of an agent and its common sense reasoning abilities can improve the performance and intelligence using SMCA (Society of Mind Cognitive Architecture) as a case study. Consciousness is a sense of awareness about oneself and the surroundings in which the animal or human being lives. This gives a connection between a non materialistic mind and a materialistic brain. Common sense in common world is one that is immediately perceived by everyone from a given environment. A six tiered (layers) SMCA control model is designed that relies on a society of agents operating using metrics associated with the principles of artificial economics in animal cognition. SMCA is a society or collection of agents, where agents tasks implemented on testbed, demonstrates simple to complex level of consciousness and commonsense.
This research work presents analysis of Modified Sarsa learning algorithm. Modified Sarsa algorithm. State-Action-Reward-State-Action (SARSA) is an technique for learning a Markov decision process (MDP) strategy, used in for reinforcement learning int the field of artificial intelligence (AI) and machine learning (ML). The Modified SARSA Algorithm makes better actions to get better rewards. Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent's performance. This modified SARSA learning algorithm can be more suitable in EMCAP architecture. The experiments are conducted the modified SARSA Learning system gets more rewards compare to existing SARSA algorithm.
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