Antonio Chella

A cognitive architecture for artificial vision

Abstract A new cognitive architecture for artificial vision is proposed. The architecture, aimed at an autonomous intelligent system, is cognitive in the sense that several cognitive hypotheses have been postulated as guidelines for its design. The first one is the existence of a conceptual representation level between the subsymbolic level, that processes sensory data, and the linguistic level, that describes scenes by means of a high level language. The conceptual level plays the role of the interpretation domain for the symbols at the linguistic levels. A second cognitive hypothesis concerns the active role of a focus of attention mechanism in the link between the conceptual and the linguistic level: the exploration process of the perceived scene is driven by linguistic and associative expectations. This link is modeled as a time delay attractor neural network. Results are reported obtained by an experimental implementation of the architecture.

Understanding dynamic scenes

We propose a framework for the representation of visual knowledge in a robotic agent, with special attention to the understanding of dynamic scenes. According to our approach, understanding involves the generation of a high level, declarative description of the perceived world. Developing such a description requires both bottom-up, data driven processes that associate symbolic knowledge representation structures with the data coming out of a vision system, and top-down processes in which high level, symbolic information is in its turn employed to drive and further refine the interpretation of a scene. On the one hand, the computer vision community approached this problem in terms of 2D/3D shape reconstruction and of estimation of motion parameters. On the other, the AI community developed rich and expressive systems for the description of processes, events, actions and, in general, of dynamic situations. Nevertheless, these two approaches evolved separately and concentrated on different kinds of problems. We propose an architecture that integrates these two traditions in a principled way. Our assumption is that a link is missing between the two classes of representations mentioned above. In order to fill this gap, we adopt the notion of conceptual space (CS—Gardenfors (2000)), a representation where information is characterized in terms of a metric space. A CS acts as an intermediate representation between subconceptual (i.e., not yet conceptually categorized) information, and symbolically organized knowledge. The concepts of process and action have immediate characterizations in terms of structures in the conceptual space. The architecture is illustrated with reference to an experimental setup based on a vision system operating in a scenario with moving and interacting people.

Patterns reuse in the PASSI methodology

Design patterns already proved successful in lowering the development time and number of errors of object-oriented software; now, they are, candidate to play a similar role in the MAS (multi-agent system) context. In this work we describe our experiences in the identification, production and application of patterns for agents. Some patterns are described together with the classification criteria and documentation approach we adopt. Upon them, we base a pattern reuse process that can be considered one of the distinguishing elements of the design methodology (PASSI) we use to develop MAS. Patterns can be applied to an existing agent or used to produce a new one with the support of a specific web based application that can read both the JAVA source code and XMI representation of the agent design documentation. After the successful application of the desired pattern(s), the source code and the design diagrams (usually a structural and dynamic diagram) of the agent can be exported. Some experimental results are reported in order to demonstrate the utility of this approach in automatically producing an interesting percentage of code lines.

Anchoring symbols to conceptual spaces: the case of dynamic scenarios

Abstract This paper deals with the anchoring of one of the most influential symbolic formalisms used in cognitive robotics, namely the situation calculus , to a conceptual representation of dynamic scenarios. Our proposal is developed with reference to a cognitive architecture for robot vision. An experimental setup is presented, aimed at obtaining intelligent monitoring operations of a robotic finger starting from visual data.

An architecture for autonomous agents exploiting conceptual representations

An architecture for autonomous agents is proposed that integrates the functional and the behavioral approaches to robotics. The integration is based on the introduction of a conceptual level, linking together a subconceptual, behavioral, level, and a linguistic level, encompassing symbolic representation and data processing. The proposed architecture is described with reference to an experimental setup, in which the robot task is that of building a significant description of its working environment.

A cognitive framework for imitation learning

Abstract In order to have a robotic system able to effectively learn by imitation, and not merely reproduce the movements of a human teacher, the system should have the capabilities of deeply understanding the perceived actions to be imitated. This paper deals with the development of cognitive architecture for learning by imitation in which a rich conceptual representation of the observed actions is built. The purpose of the following discussion is to show how this Conceptual Area can be employed to efficiently organize perceptual data, to learn movement primitives from human demonstration and to generate complex actions by combining and sequencing simpler ones. The proposed architecture has been tested on a robotic system composed of a PUMA 200 industrial manipulator and an anthropomorphic robotic hand.

A possible approach to the development of robotic multi-agent systems

The design of an agent system for robotics is a problem that involves aspects coming from many different disciplines (robotics, artificial intelligence, computer vision, software engineering). The most difficult part of it, often consists in producing and tuning the algorithms that incorporates the robot behavior (planning, obstacle avoidance,...) and abilities (vision, manipulation, navigation,...). Frequently, the reuse of these parts is left to a copy and paste procedure from previous applications to the new one. In so doing many problems could arise. We propose a comprehensive approach for multi-agent systems oriented to robotics applications that uses a complete design methodology supported by a specific design tools and a pattern repository that interacting each other and with the designer allow the production of a coherent design that easily incorporates patterns coming from previously experienced features and automatically produces a large part of the final code.

Biologically Inspired Cognitive Architectures 2012

The conference on “ Biologically Inspired Cognitive – Architectures (BICA) 2012” was held in Palermo, Italy from October 31 to November 2. It has been the third annual meeting of the BICA Society and the fifth annual BICA meeting. (Chella et al. 2012) The series of BICA conferences started in 2008 under the umbrella of the Association for the Advancement of Artificial Intelligence (AAAI). In 2010, the BICA Society was incorporated as a nonprofit organization – a scientific society with headquarters in the United States, with the mission of promoting and facilitating the transdisciplinary study of BICA (Samsonovich et al. 2010, Samsonovich 2012).a aSee also the BICA website http://bicasociety.org/

A cognitive architecture for robot self-consciousness

OBJECTIVE One of the major topics towards robot consciousness is to give a robot the capabilities of self-consciousness. We propose that robot self-consciousness is based on higher order perception of the robot, in the sense that first-order robot perception is the immediate perception of the outer world, while higher order perception is the perception of the inner world of the robot. METHODS AND MATERIAL We refer to a robot cognitive architecture that has been developed during almost 10 years at the RoboticsLab of the University of Palermo. The architecture is organized in three computational areas. The subconceptual area is concerned with the low level processing of perceptual data coming from the sensors. In the linguistic area, representation and processing are based on a logic formalism. In the conceptual area, the data coming from the subconceptual area are organized in conceptual categories. RESULTS To model higher order perceptions in self-reflective agents, we introduce the notion of second-order points in conceptual space. Each point in this space corresponds to a self-reflective agent, i.e., the robot itself, persons, and other robots with introspective capabilities. CONCLUSIONS The described model of robot self-consciousness, although effective, highlights open problems from the point of view of the computational requirements of the current state-of-art computer systems. Some future works that lets the robot to summarize its own past experiences should be investigated.

Conceptual Spaces for Computer Vision Representations

A framework for high-level representations in computer vision architectures is described. The framework is based on the notion of conceptual space. This approach allows us to define a conceptual semantics for the symbolic representations of the vision system. In this way, the semantics of the symbols can be grounded to the data coming from the sensors. In addition, the proposed approach generalizes the most popular frameworks adopted in computer vision.