One of the Internet’s greatest strengths is that it does not know or care what its applications are or what they are doing: it simply forwards data. Yet network users experience the network through the functioning and performance of applications. This divergence of perspective leads to a number of problems. For example, a user whose local DNS service has failed may perceive the network as broken, even though from a network perspective, data continues to flow correctly. If an email server or a Web server fails, the user will say the network is broken; the network operator will say the network is fine. We need a way to make the network more aware of itself and its applications, without destroying the open and transparent data plane. To meet this need we propose the creation of an Internet knowledge plane. The knowledge plane is a distributed and decentralized construct within the network that gathers, aggregates, and manages information about network behavior and operation, and provides an integrated view to all parties (operators, users, and the network itself). The goal is to enlarge our view of what constitutes the network to match the intuition of a user, and to enhance our ability to manage the network intelligently, without disturbing the open and unknowing forwarding plane. The knowledge plane is intelligent: it can reason about the network’s behavior and act upon the results of its reasoning. It can remember and learn from past behavior. To achieve that goal, we propose to adapt and employ recent work in cognition such as the separation of algorithm, policy and goals, and new models for knowledge representation.

Many intellectual disciplines contribute to the field of cognitive science: psychology, linguistics, anthropology, and artificial intelligence, to name just a few. Cognitive science itself

ABSTRACT This article addresses issues in developing cognitive architectures—generic computational models of cognition. Cognitive architectures are believed to be essential in advancing understanding of the mind, and therefore, developing cognitive architectures is an extremely important enterprise in cognitive science. The article proposes a set of essential desiderata for developing cognitive architectures. It then moves on to discuss in detail some of these desiderata and their associated concepts and ideas relevant to developing better cognitive architectures. It argues for the importance of taking into full consideration these desiderata in developing future architectures that are more cognitively and ecologically realistic. A brief and preliminary evaluation of existing cognitive architectures is attempted on the basis of these ideas. 1.

Abstract — We describe an embodied cognitive system based on a three-level architecture that includes a sensorimotor layer, a mid-level layer that stores and reasons about object-action action plans to be realised and possibly further specified by the lower levels. The system works in two modes, exploration and plan execution, that both make use of the same architecture. We give results of different sub-processes as well as their interaction. In particular, we describe the generation and execution of plans as well as a set of learning processes that take place independently of, or in parallel with, plan execution. I.

Summary. After identifying the key challenges of knowledge management and proposing a vision that address them, this paper explores how cognitive agents can be used to design management systems that implement this vision and that in particular support the knowledge management processes in both their social, organizational and individual dimension.

Some AI researchers aim to make useful machines, including robots. Others aim to understand general principles of information-processing machines with various kinds of intelligence, whether natural or artificial, including humans and human-like systems. They primarily address scientific and philosophical questions rather than practical goals. However, the tasks required to pursue scientific and engineering goals overlap, since both involve building working systems to test ideas and demonstrate results, and the conceptual frameworks and development tools needed for both overlap. This paper, partly based on philosophical analysis of requirements for robots in complex 3-D environments, surveys varieties of meta-cognition, drawing attention to requirements that drove biological evolution and which are also relevant to ambitious engineering goals.

This paper discusses our views on the future of the field of cognitive architectures, and how the scientific questions that define it should be addressed. We also report on a set of requirements, and a related architecture design, that we are currently investigating as part of the CoSy project. 1 What Are Architectures? The first problem we face as researchers in the field of cognitive architectures is defining exactly what we are studying. This is important because the term “architecture ” is so widely used in modern technological fields. An agent’s cognitive architecture defines the information-processing components within the “mind ” of the agent, and how these components are structured in relation to each other. Also, there is a close link between architectures and the mechanisms and representations used within them (where

The paper presents an approach to utterance planning, which can dynamically use context information about the environment in which a dialogue is situated. The approach is functional in nature, using systemic networks to specify its planning grammar. The planner takes a description of a communicative goal as input, and produces one or more logical forms that can express that goal in a contextually appropriate way. Both the goal and the resulting logical forms are expressed in a single formalism as ontologically rich, relational structures. To realize the logical forms, OpenCCG is used. The paper focuses primarily on the implementation, but also discusses how the planning grammar can be based on the grammar used in OpenCCG, and trained on (parseable) data. 1

GLAIR (Grounded Layered Architecture with Integrated Reasoning) is a multi-layered cognitive architecture for embodied agents operating in real, virtual, or simulated environments containing other agents. The highest layer of the GLAIR Architecture, the Knowledge Layer (KL), contains the beliefs of the agent, and is the layer in which conscious reasoning, planning, and act selection is performed. The lowest layer of the GLAIR Architecture, the Sensori-Actuator Layer (SAL), contains the controllers of the sensors and effectors of the hardware or software robot. Between the KL and the SAL is the Perceptuo-Motor Layer (PML), which grounds the KL symbols in perceptual structures and subconscious actions, contains various registers for providing the agent’s sense of situatedness in the environment, and handles translation and communication between the KL and the SAL. The motivation for the development of GLAIR has been “Computational Philosophy”, the computational understanding and implementation of human-level intelligent behavior without necessarily being bound by the actual implementation of the human mind. Nevertheless, the approach has been inspired by human psychology and biology.

Abstract. We discuss some rough set tools for perception modelling that have been developed in our project for a system for modelling networks of classifiers for compound concepts. Such networks make it possible to recognize behavioral patterns of objects and their parts changing over time. We present a method that we call a method for on-line elimination of non-relevant parts (ENP). This method was developed for on-line elimination of complex object parts that are irrelevant for identifying a given behavioral pattern. Some results of experiments with data from the road simulator are included. 1