Opinions expressed in this report are those 3. COUNT possesses a repertoire of of the authors, and do not necessarily

He knows about examples and heuristics and how they are related. He has a sense of what to use and when to use it, and what is worth remembering. He has an intuitive feeling for the subject, how it hangs together, and how it relates to other theories. He knows how not to be swamped by details, but also to reference them when he needs them. This paper is concerned with this important extra-logical knowledge that is often outside of traditional discussions in mathematics.

This talk is about "knowledge representation," particularly some of the fundamental assumptions in' olved in the way we handle knowledge in current Al and cognitive science research. The whole enterprise seems to have at-.'andoned Namely, we have a:7ccd to assume that knowledge is something that can be represelted--that knowing something means having a data structure stored away that stands for what is known. In other words, we treat knowledge as an object, a representable object. The advantage of this way of looking at things is that there is a very simple relationship between the things we say a person or system knows, and the memories, knowledge, or data structures we say he or it has stored.

Although in both economics and cognitive science the meaning-bearing in generative grammar), decision theory, and even objects (or the instantiation of the symbols) are physical, parts of anthropology (eg, Levi-Strauss). The functionalist it is only by referring to their symbolic or referential Perspective 2 is now quite general in psychology and philosophy character that we can explain the observed regularities in of mind as well as in engineering, where it is the resulting behavior.

For natural and artificial systems with some symmetry structure, computational understanding and manipulation can be achieved without learning by exploiting the algebraic structure. Here we describe this algebraic coordinatization method and apply it to permutation puzzles. Coordinatization yields a structural understanding, not just solutions for the puzzles.

COGSCI2013, the 35th annual meeting of the Cognitive Science Society and the first to take place in Germany, was held from the 31st of July to the 3rd of August. Cognitive scientists with varied backgrounds gathered in Berlin to report and discuss on expanding lines of research, spanning multiple fields but striving in one direction: to understand cognition with all its properties and peculiarities. A rich program featuring keynotes, symposia, workshops and tutorials, along regular oral and poster sessions, offered the attendees a vivid and exciting overview of where the discipline is going while serving as a fertile forum of interdisciplinary discussion and exchange. This report attempts to point out why this should matter to artificial intelligence as a whole.

COGSCI2013, the 35th annual meeting of the Cognitive Science Society and the first to take place in Germany, was held from the 31st of July to the 3rd of August. Cognitive scientists with varied backgrounds gathered in Berlin to report and discuss on expanding lines of research, spanning multiple fields but striving in one direction: to understand cognition with all its properties and peculiarities. A rich program featuring keynotes, symposia, workshops and tutorials, along regular oral and poster sessions, offered the attendees a vivid and exciting overview of where the discipline is going while serving as a fertile forum of interdisciplinary discussion and exchange. This report attempts to point out why this should matter to artificial intelligence as a whole.

Video Surveillance is a fast evolving field of research and development (R&D) driven by the urgent need for public security and safety (due to the growing threats of terrorism, vandalism, and anti-social behavior). Traditionally, surveillance systems are comprised of two components - video cameras distributed over the guarded area and human observer watching and analyzing the incoming video. Explosive growth of installed cameras and limited human operator's ability to process the delivered video content raise an urgent demand for developing surveillance systems with human like cognitive capabilities, that is - Cognitive surveillance systems. The growing interest in this issue is testified by the tens of workshops, symposiums and conferences held over the world each year. The IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS) is certainly one of them. However, for unknown reasons, the term Cognitive Surveillance does never appear among its topics. As to me, the explanation for this is simple - the complexity and the indefinable nature of the term "Cognition". In this paper, I am trying to resolve the problem providing a novel definition of cognition equally suitable for biological as well as technological applications. I hope my humble efforts will be helpful.

These notes describe how the "SP theory of intelligence", and its embodiment in the "SP machine", may help to realise cognitive computing, as described in the book "Smart Machines". In the SP system, information compression and a concept of "multiple alignment" are centre stage. The system is designed to integrate such things as unsupervised learning, pattern recognition, probabilistic reasoning, and more. It may help to overcome the problem of variety in big data, it may serve in pattern recognition and in the unsupervised learning of structure in data, and it may facilitate the management and transmission of big data. There is potential, via information compression, for substantial gains in computational efficiency, especially in the use of energy. The SP system may help to realise data-centric computing, perhaps via a development of Hebb's concept of a "cell assembly", or via the use of light or DNA for the processing of information. It has potential in the management of errors and uncertainty in data, in medical diagnosis, in processing streams of data, and in promoting adaptability in robots.

This panel discussion brings focuses the attention of AI researchers and other cognitive scientists on natural language. How can language be studied using integrated cognitive architectures? What do such architectures have to say about traditional questions in language and language processing?