@MISC\{IMM2008-05705,
    author       = "J. Larsen",
    title        = "Cognitive Systems",
    year         = "2008",
    month        = "oct",
    keywords     = "cogntive systems, machine learning, cognitve architecture, examples",
    publisher    = "Department of Informatics and Mathematical Modelling",
    address      = "Richard Petersens Plads, Building 321",
    note         = "Tutorial presented at MLSP2008 16.10.2008. The associated {ZIP} file contatains a {PDF} hand-out version.",
    url          = "http://mlsp2008.conwiz.dk/index.php?id=65",
    abstract     = "The tutorial will discuss the definition of cognitive systems as the possibilities to extend the current systems engineering paradigm in order to perceive, learn, reason and interact robustly in open-ended changing environments. I will also address cognitive systems in a historical perspective and its relation and potential over current artificial intelligence architectures. 

Machine learning models that learn from data and previous knowledge will play an increasingly important role in all levels of cognition as large real world digital environments (such as the Internet) usually are too complex to be modeled within a limited set of predefined specifications. There will inevitably be a need for robust decisions and behaviors in novel situations that include handling of conflicts and ambiguities based on the capability and knowledge of the artificial cognitive system. 

Further, there is a need for automatic extraction and organization of meaning, purpose, and intentions in interplay with the environment (machines, artifacts and users) beyond current systems with build-in semantic representations and ontologies—in particular in terms of the interaction with users (users-in-the-loop models) through user models and user interaction models. 

Research in cognitive information processing is inherently multi-disciplinary and involves natural science and technical disciplines, e.g., control, automation, and robot research, physics and computer science, as well as humanities such as social sciences, cognitive psychology, and semantics. However, machine learning for signal processing plays a key role at all the levels of the cognitive processes, and we expect this to be a new emerging trend in our community in the coming years. 

Current examples of the use of machine learning for signal processing in cognitive systems include e.g. personalized information systems, sensor network systems, social dynamics system and Web2.{0,} and cognitive components analysis. I will use example from our own research and link to other research activities."
}