In the past years we developed several design processes (Chella et Perception, also including memory, is one of the most important al. 2006)(Cossentino and Seidita 2004)(Cossentino, Gaglio, features a robotic system must present. In (Chella and Seidita) following the approach based on Situational and Manzotti 2007) it is argued that a perception process can Method Engineering paradigm we fixed in these years be modelled and implemented as a continuous interaction (Cossentino et al. 2007)(Seidita et al. 2009). In the following loop among brain, body and environment; by continuously subsections an overview on the used SME approach, comparing actual and expected "data" coming from the environment the PASSI design process, and the robot perception loop will the robot achieves the ability to gain perceptual be given.

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.

Boid flocking is a system in which several individual agents follow three simple rules to generate swarm-level flocking behavior. To control this system, the user must adjust the agent program parameters, which indirectly modifies the flocking behavior. This is unintuitive because the properties of the flocking behavior are non-explicit in the agent program. In this paper, we discuss a domain-independent approach for detecting and controlling two emergent properties of boids: density and a qualitative threshold effect of swarming vs. flocking. Also, we discuss the possibility of applying this approach to detecting and controlling traffic jams in traffic simulations.

Time perception and inferences there from are of critical importance to many autonomous agents. But time is not perceived directly by any sensory organ. We argue that time is constructed by cognitive processes. Here we present a model for time perception that concentrates on succession and duration, and that generates these concepts and others, such as continuity, immediate present duration, and lengths of time. These concepts are grounded through the perceptual process itself. The LIDA cognitive model is used to illustrate these ideas.

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.

Virtual patients are viewed as a cost-effective alternative to standardized patients for role-play training of clinical interviewing skills. However, training studies produce mixed results. Students give high ratings to practice with virtual patients and feel more self-confident, but they show little improvement in objective skills. This confidence-competence gap matches a common cognitive illusion, in which students overestimate the effectiveness of training that is too easy. We hypothesize that cost-effective training requires virtual patients that emphasize functional and psychological fidelity over physical fidelity. We discuss 12 design decisions aimed at cost-effective training and their application in virtual patients for practicing brief intervention in alcohol abuse. Our STAR Workshop includes 3 such patients and a virtual coach. A controlled experiment evaluated STAR and compared it to an easier E-Book and no-training Control. E-Book subjects displayed the illusion, giving high ratings to their training and self-confidence, but performing no better than Control subjects on skills. STAR subjects gave high ratings to their training and self-confidence and scored better higher than E-Book or Control subjects on skills. We invite other researchers to use the underlying Imp technology to build virtual patients for their own work.

LODE is a web tool for children that are novice readers, and is primarily meant for deaf children. It proposes written stories and interactive games for reasoning, globally, on the stories. In this paper, first, we motivate the rationale of LODE, and explain its reasoning games. Then we briefly describe the design of the web client-server architecture of LODE; the server employs a constraint programming system for creating and solving the LODE games in real time. Finally, we concentrate on two evaluations of the latest prototype of LODE: one with hearing novice readers; another one with deaf readers. We conclude by discussing the results of the evaluations, and their implications for LODE.

The challenge of designing a human-level learner is central to creating a computational equivalent of the human mind. It demands the level of robustness and flexibility of learning that is still only available in biological systems. Therefore, it is essential that we better understand at a computational level how biological systems naturally develop their cognitive and learning functions. In recent years, biologically inspired cognitive architectures (BICA) have emerged as a powerful new approach toward gaining this kind of understanding. The impressive success of BICA-2008 was clear evidence of this trend. As the second event in the series, BICA-2009 continues our attack on the challenge, with the overall atmosphere of excitement and promise, brainstorming, and collaboration.

A self-organizing neural network is described that can associate between different modalities and also has the ability to learn perceptual sequences. This architecture is a step towards the development of a complete agent containing simplified versions of all major neural subsystems in a mammal. It aims at exploring as well as takes inspiration from the idea that cognitive function involves an internal simulation of perception and movement. We have tested the architecture in simulations as well as together with real sensors with very encouraging results.

In this paper, we present a cognitive model of frequency-based decision-making applied to the task of landmine detection. The model is implemented in the ACT-R cognitive architecture and is strongly constrained by the cognitive primitives of the architecture. We then generalize the model to another task in the domain of macroeconomic decision-making using the same architecture, pursuing theoretical parsimony. We describe each model's representation requirements, assess their fits to the data, and analyze their performance scaling as a function of task and architectural parameters. Efforts to generalize the landmine detection model to macroeconomic decision making showed that reasonable fits to the macro-economic performance data could be achieved by models based either on procedural knowledge or declarative knowledge. This finding underscores the importance of distinguishing between processing strategies employed to execute tasks. Such detail appears needed to understand the neural foundations of frequency-based decision-making.