Issues concerning students involved with online learning paths, that need to be faced by e-Tutors on their day-to-day activity, most often than not fall into known pedagogical patterns - that are problems and difficulties already occurred in the past and dealt with. These pedagogical patterns belong to e-Tutors' know-how and experience and their resolution are frequently a matter of activating routine processes or giving  pre-factored answers; nevertheless statistical data indicates that these issues consume a considerable slice of tutors' time. While a portion of the scientific community is still devoting much effort in developing artificial tutoring systems - by deploying AI/MAS-enabled technologies - the solution being investigated by our team focuses on enhancing already-available, open source LMS by implementing a general-purpose tracking and monitoring toolkit able to support e-Tutors in recognizing and dealing with pedagogical patterns stored into a decentralised Knowledge Base. The system architecture is designed to house multiple platforms (only one adapter interface needs to be written for each LMS) and is able to perform real-time, as well as scheduled, data collection by means of Jade-based agents and schedulers.  Information obtained from the processed data is then returned to the platform via web services and specific interfaces (instant messaging chatbot). The first deployed prototype is currently being experimented in adult higher education learning paths and is able to track student activity, forum readings and writings and offers a basic chat-based help interface. Our aim is to turn a standard LMS into a knowledge aggregator where information about its users, its contents and interactions between the two can be mined via Knowledge Services; resulting data could then be used to refine users' and groups' profiles, to monitor learners' deviance from expected learning path, and ultimately to adjust the applied pedagogical model.

This paper describes the DynaLearn project, which seeks to address contemporary problems in science education by integrating well established, but currently independent technological developments, and utilize the added value that emerges. Specifically, diagrammatic representations are used for learners to articulate, analyse and communicate ideas, and thereby construct their conceptual knowledge. Ontology mapping is used to find and match co-learners working on similar ideas to provide individualised and mutually benefiting learning opportunities. Virtual characters are used to make the interaction engaging and motivating. The development of the workbench is tuned to fit key topics from environmental science curricula, and evaluated and further improved in the context of existing curricula using case studies. Through this approach, the DynaLearn project will deliver an individualised and engaging cognitive tool for acquiring conceptual knowledge that fits the true nature of this expertise.

We have developed computer environments that support learning by teaching and the use of self regulated learning (SRL) skills through interactions with virtual agents. More specifically, students teach a computer agent, Betty, and can monitor her progress by asking her questions and getting her to take quizzes. The system provides SRL support via dialog-embedded prompts by Betty, the teachable agent, and Mr. Davis, the mentor agent. Our primary goals have been to support learning in complex science domains and facilitate development of metacognitive skills. More recently, we have also employed sequence analysis schemes and hidden Markov model (HMM) methods for assigning context to and deriving aggregated student behavior sequences from activity data. These techniques allow us to go beyond analyses of individual behaviors, instead examining how these behaviors cohere in larger patterns. We discuss the information derived from these models, and draw inferences on students’ use of self-regulated learning strategies.

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.

We describe some experiments which simulate a grounded approach to the acquisition of the cognitive and linguistic competence required to communicate propositional logic sentences. This encompasses both the construction of a conceptualisation of its environment by each individual agent and of a shared language by the population. The processes of conceptualisation and language acquisition in each individual agent are based on general purpose cognitive capacities, such as categorisation, discrimination, invention, adoption and induction. The construction of a shared language by the population is achieved using a particular type of linguistic interaction, known as the evaluation game, which gives rise to a common set of linguistic conventions through a process of self-organisation. This work addresses the problem of the acquisition of both the semantics and the syntax of propositional logic. Trying to learn these two aspects at the same time is more difficult than learning the semantics or the syntax of propositional logic separately. Because the agents must coordinate their linguistic behaviour taking into account only the subset of objects which constitutes the topic of a particular linguistic interaction. This means that a pair of agents can communicate successfully about a particular subset of objects (a topic) even if they use different conceptualisations (formulas) in order to identify the same topic. And this introduces a high degree of ambiguity in the interpretation process the agents have to deal with when they try to construct a shared communication language. In spite of this, the results of the experiments show that at the end of the simulation runs the individual agents build different conceptualisations and grammars, but that the conceptualisations and grammars of the agents in the population are compatible in the sense that they guarantee the unambiguous communication of propositional logic sentences.

A true human-level learner should be able to deliberately construct its own knowledge, its processes of reasoning resulting in a new knowledge, its system of values and goals, and the scenario of its cognitive growth. These capabilities require a cognitive architecture of a new kind that supports metacognition, self-awareness and self-regulation. An example architecture design called Constructor is described in this work. The main distinguishing feature of this architecture is its virtually unlimited self-regulated cognitive growth ability. Other features include metacognition, self-awareness, and an intrinsic embodiment in virtual reality that is used, e.g., for active construction of cognitive and learning processes.

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.

We consider directed graphs over a set of n agents, where an edge (i,j) is taken to mean that agent i supports or trusts agent j. Given such a graph and an integer k\leq n, we wish to select a subset of k agents that maximizes the sum of indegrees, i.e., a subset of k most popular or most trusted agents. At the same time we assume that each individual agent is only interested in being selected, and may misreport its outgoing edges to this end. This problem formulation captures realistic scenarios where agents choose among themselves, which can be found in the context of Internet search, social networks like Twitter, or reputation systems like Epinions. Our goal is to design mechanisms without payments that map each graph to a k-subset of agents to be selected and satisfy the following two constraints: strategyproofness, i.e., agents cannot benefit from misreporting their outgoing edges, and approximate optimality, i.e., the sum of indegrees of the selected subset of agents is always close to optimal. Our first main result is a surprising impossibility: for k \in {1,...,n-1}, no deterministic strategyproof mechanism can provide a finite approximation ratio. Our second main result is a randomized strategyproof mechanism with an approximation ratio that is bounded from above by four for any value of k, and approaches one as k grows.

Biologically inspired computing is an area of computer science which uses the advantageous properties of biological systems. It is the amalgamation of computational intelligence and collective intelligence. Biologically inspired mechanisms have already proved successful in achieving major advances in a wide range of problems in computing and communication systems. The consortium of bio-inspired computing are artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, fractal geometry, DNA computing and quantum computing, etc. This article gives an introduction to swarm intelligence.

This paper deals with computational approaches to storytelling, or the production of stories by computers, with a particular attention on the way human creativity is modelled or emulated, also in computational terms. Features relevant to creativity and to stories are analysed, and existing systems are reviewed under the light of that analysis.The extent to which they implement the key features proposed in recent models of computational creativity is discussed. Limitations, avenues of future research and expected trends are outlined.