Abstract—Autonomic computing systems must be able to detect and respond to errant behavior or changing conditions with little or no human intervention. Clearly, decision making is a critical issue in such systems, which must learn how and when to invoke corrective actions based on past experience. This paper describes the design, implementation, and evaluation of MESO, a pattern classifier designed to support online, incremental learning and decision making in autonomic systems. A novel feature of MESO is its use of small agglomerative clusters, called sensitivity spheres, that aggregate similar training samples. Sensitivity spheres are partitioned into sets during the construction of a memory-efficient hierarchical data structure. This structure facilitates data compression, which is important to many autonomic systems. Results are presented demonstrating that MESO achieves high accuracy while enabling rapid incremental training and classification. A case study is described in which MESO enables a mobile computing application to learn, by imitation, user preferences for balancing wireless network packet loss and bandwidth consumption. Once trained, the application can autonomously adjust error control parameters as needed while the user roams about a wireless cell. Index Terms—Autonomic computing, adaptive software, pattern classification, decision making, imitative learning, machine learning, mobile computing, perceptual memory, reinforcement learning. Ç

The authors introduce an ontology for the study of how animals think, as well as a comprehensive model of human and animal cognition utilizing the ontology. The IDA (Intelligent Distribution Agent) model of cognition, a computational and conceptual model derived from a working software agent, is described within the framework of the ontology. The model is built on functional needs of animals, relating it to the existing literature. The article provides testable hypotheses and a sample a model of decision-making processes in voles. The article closes with a brief comparison of the IDA model to other computational models of cognition, and a discussion of the strengths and weaknesses of the ontology and the model.

Memory (SDM) is a model of an associative memory based on the properties of a high dimensional binary space. This model has received some attention from researchers of different areas and has been improved over time. However, a few problems have to be solved when using it in practice, due to the non-randomness characteristics of the actual data. We tested an SDM using different forms of encoding the information, and in two different domains: robot navigation and manipulation. Our results show that the performance of the SDM in the two domains is affected by the way the information is actually encoded, and may be improved by some small changes in the model. Index Terms—Sparse distributed memory (SDM), EPIROME, episodic memory, associative memory, navigation, manipulation, robotics. I.

2 Recently there has been a resurgence of interest in general, comprehensive models of human cognition. Such models aim to explain higher order cognitive faculties, such as deliberation and planning. Given a computational representation, the validity of these models can be tested in computer simulations such as software agents or embodied robots. The push to implement computational models of this kind has created the field of Artificial General Intelligence, or AGI. Moral decision making is arguably one of the most challenging tasks for computational approaches to higher order cognition. The need for increasingly autonomous artificial agents to factor moral considerations into their choices and actions has given rise to another new field of inquiry variously known as Machine Morality, Machine Ethics, Roboethics or Friendly AI. In this paper we discuss how LIDA, an AGI model of human cognition, can be adapted to model both affective and rational features of moral decision making. Using the LIDA model we will demonstrate how moral decisions can be made in many domains using the same mechanisms that