The development of artificial intelligence systems has to date been largely one of manual labor. This Constructionist approach to A.I. has resulted in a diverse set of isolated solutions to relatively small problems. Small success stories of putting these pieces together in robotics, for example, has made people optimistic that continuing on this path would lead to artificial general intelligence. This is unlikely. "The A.I. problem" has been divided up without much guidance from science or theory, resulting in a fragmentation of the research community and a set of grossly incompatible approaches. Standard software development methods come with serious limitations in scaling; in A.I. the Constructionist approach results in systems with limited domain application and severe performance brittleness. Genuine integration, as required for general intelligence, is therefore practically and theoretically precluded. Yet going beyond current A.I. systems requires significantly more complex integration than attempted to date, especially regarding transversal functions such as attention and learning. The only way to address the challenge is replacing top-down architectural design as a major development methodology with methods focusing on self-generated code and self-organizing architectures. I call this Constructivist A.I., in reference to the self-constructive principles on which it must be based. Methodologies employed for Constructivist A.I. will be very different from today's software development methods. In this paper I describe the argument in detail and examine some of the implications of this impending paradigm shift.

An understanding of the policy and resource availability constraints under which others operate is important for effectively developing and resourcing plans in a multi-agent context. Such constraints (or norms) are not necessarily public knowledge, even within a team of collaborating agents. What is required are mechanisms to enable agents to keep track of who might have and be willing to provide the resources required for enacting a plan by modeling the policies of others regarding resource use, information provision, etc. We propose a technique that combines machine learning and argumentation for identifying and modeling the policies of others. Furthermore, we demonstrate the utility of this novel combination of techniques through empirical evaluation.

Recent research identifies stochastic dominance as critical for understanding the relationship between network structure and diffusion. This paper introduces the concept of stochastic dominance, explains the theory linking stochastic dominance and diffusion, and applies this theory to a number of diffusion studies in the literature. The paper illustrates how the theory connects observations from different disciplines, and details when and how those observations can be generalized to broader classes of networks.

Memory retrieval is a spontaneous process difficult to measure in naturalistic settings. By adapting an automated paging process, we measured spontaneous autobiographical and prospective memory retrieval probability, and found the derived frequency of recall in a given time period to be significantly higher than expected. Altogether, this research provides a quantitative characterization of human memory.

In the The use of argumentation for decision support is not new, remainder of the paper, we motivate our approach by using a with a long history of studies such as (Amgoud and Prade group decision making setting in clinical oncology, present a 2009; Amgoud and Vesic 2009; Amgoud, Dimopoulos, and formal framework, and procedural basis for mixed initiative Moraitis 2008; Fox et al. 2007; Amgoud and Prade 2006; argumentation and finally describe a clinical group decision Atkinson, Bench-Capon, and Modgil 2006; Rehg, McBurney, support system that implements this framework.

In discussions about the physical support of conscious experience, a recent trend has been introduced (by Tononi and various colleagues) that measures the capacity of a network to discriminate among different states and integrate the information generated by this discrimination. This capacity to generate and integrate information can be used to understand the information processing in a network and Tononi has claimed that it is also linked to conscious experience. This paper describes experiments in which networks of weightless neurons were used to explore how different connection patterns and architectures affected the effective information generated by a network. The training of these networks using easily recognizable images made it easy to monitor their internal states, and this supports the interpretation of the system using the mental stance, which is described in a companion paper. By applying the same training to different architectures we were also able to study how the informational relationships depended on a combination of training and other dynamic effects.

The availability of online documents coupled with emergent text mining methods has opened new research horizons. To achieve their potential, mining technologies need to be theoretically focused. We present data theory as a crucial component of text mining, and provide a substantive proto- theory from the synthesis of complex multigames, prototype concepts, and emotio-cognitive orientation fields. We discuss how the data theory presented informs the application of text mining to mining discourse(s) and how, in turn, this allows for modeling across contextual thresholds. Finally, the relationship between discourse mining, data theory, and thresholds is illustrated with an historical example, the events surrounding the 1992 civil war in Tajikistan.

Creating new concepts from data is a hard problem in the development of cognitive architectures, but one that must be solved for the BICA community to declare success.  Two concept generation algorithms are presented here that are appropriate to different levels of concept abstraction: state-space partitioning with decision trees and context-based similarity.

Insufficient knowledge and resources is not only a biological constraint on human and animal intelligence, but also has important functional implications for artificial intelligence (AI) systems. Traditional theories dominating AI research typically assume some kind of sufficiency of knowledge and resources, so cannot solve many problems in the field. AI needs new theories obeying this constraint, which cannot be obtained by minor revisions or extensions of the traditional theories. The practice of NARS, an AI project, shows that such new theories are feasible and promising in providing a new theoretical foundation for AI.

Oscillatory neural networks have been an increasing focus of study over the last several years. Here we consider simple oscillatory memories for short-term retention of items occurring as temporal sequences. By incorporating decay as well as interference, we find that it is easy to match behavioral data from human subjects recalling temporal sequences under different situations by adjusting a single parameter in the model. These results suggest that simple oscillatory memories capture at least some key properties of human short-term memory, and might be used effectively in future biologically-inspired cognitive architectures.