People acquire new knowledge in various ways and this helps them to adapt to changing environment properly. In this paper, we investigatethe interoperation of multiple learning mechanisms within a single system. We extend a cognitive architecture, ICARUS, to have three different modes of learning. Through experiments in a modified Blocks World and a route generation domain, we test and demonstrate the system's ability to get synergistic effects from these learning mechanisms.

Learning to use an object as a tool requires understanding what goals it helps to achieve, the properties of the tool that make it useful and how the tool must be manipulated to achieve the goal. We present a method that allows a robot to learn about objects in this way and thereby employ them as tools. An initial hypothesis for an action model of tool use is created by observing another agent accomplishing a task using a tool. The robot then refines its hypothesis by active learning, generating new experiments and observing the outcomes. Hypotheses are updated using Inductive Logic Programming. One of the novel aspects of this work is the method used to select experiments so that the search through the hypothesis space is minimised.

In most cognitive architectures, episodic memory is either not implemented, or plays a secondary role. In contrast, in the Xapagy architecture episodic memory is the primary means of acquiring and using knowledge. Shadowing, the main reasoning method of the system, relies on unprocessed historical recordings of concrete events to determine the agent's behavior. This paper outlines the use of episodic memory in Xapagy, and investigates whether episodic memory might play a wider role in cognitive architectures at large.

According to Baddeley, "The episodic buffer is assumed to be a limitedcapacity Our research team has been working on the development of a language analysis model (Ball, 2011; Ball, Heiberg & temporary storage system that is capable of Silber, 2007) within the ACT-R cognitive architecture integrating information from a variety of sources…the (Anderson, 2007) since 2002 (Ball, 2004). The focus is on buffer provides not only a mechanism for modeling the development of a general-purpose, large-scale, functional environment, but also for creating new cognitive model (Ball, 2008; Ball et al., 2010) that adheres to well representations" (ibid, p. 421). A key empirical result which established cognitive constraints on human language motivated Baddeley to introduce the episodic buffer after 25 processing (HLP) as realized by ACT-R.

A critical prerequisite for human-level cognitive systems is having a rich conceptual understanding of the world. We describe a system that learns conceptual knowledge by deep understanding of WordNet glosses. While WordNet is often criticized for having a too fine-grained approach to word senses, the set of glosses do generally capture useful knowledge about the world and encode a substantial knowledge base about everyday concepts. Unlike previous approaches that have built ontologies of atomic concepts from the provided WordNet hierarchies, we construct complex concepts compositionally using description logic and perform reasoning to derive the best classification of knowledge. We view this work as simultaneously accomplishing two goals: building a rich semantic lexicon useful for natural language processing, and building a knowledge base that encodes common-sense knowledge.

In this paper, we propose a simulation platform for the performance testing of robots and human operations. Robots have been used in disaster scenarios, where the environment is unstable. Human operators may have no prior experience in dealing with such dynamically changing environments, which may also be unstable for robotic tasks. To develop rescue robots, disaster situation emulation and human-in-loop test platform are required in addition to robot simulators. The proposed platform is used to design, develop robots and to conduct drills for robot operations, and to carry out experiments. And the results of experiments are presented.

Shared Mental Models (SSM) are crucial for adequate coordination of activities and resource deployment in disaster responses. Both human and robot are actors in the construction of such models. Based on a situated Cognitive Engineering (sCE) methodology, we identified the needs, functions and evaluation paradigm for this model construction support. Via prototyping, some basic functions proved to be of value (e.g., hierarchical view on functions, processes and resources). Currently, more advanced functions are under investigation (e.g., observability display). The evaluations will provide the empirical foundation of the underlying SMM theory for human-robot teams.

In this paper, we discuss the design of a new “intelligent” system capable of selecting the most “outstanding” user from a group of people in a scene. This ability to select a user to interact with is very important in natural interfaces and in emergency-related applications where several people can ask to communicate simultaneousely. The system uses both static and dynamic features such as speed, height and social features (interpersonal distances) which are all acquired using a RGB-Depth camera (Kinect). Those features are combined and a contrast-based approach is able to focus the system’ attention on a specific user without complex rules. People position with respect to the Kinect sensor and learning of the previous people behavior are also used in a top-down way to influence the decision on the most interesting people. This application is represented by a wall of HAL9000's eyes that search in the scene who is the most different person then track and focus at him until someone more "different'' shows up.

Earlier Teamwork implies communication with shared references work also evaluates the robustness of ICP against low constrained and symbols. The collaboration between robot and human is environments (Rusinkiewicz and Levoy 2001). This therefore highly dependent on a common representation of was mainly done in simulation so real word datasets targeting the environment. Part of this representation is a map, either this limitations could bring the analysis farther. An other global or local, that can serve both the robot to do its own problem, recently raised in vision registration (Mortensen, task and the human to increase his situation awareness, to Deng, and Shapiro 2005), is the problem of repetitive elements collaboratively plan and observe the evolution of a situation.

In this paper we address the design of robots that can be successful partners to humans in joint activity. The paper outlines an approach to achieving adjustable autonomy during execution- and hence to achieve resilient multi-actor joint action - based on both temporal and epistemic situation projection. The approach is based on non-deterministic planning techniques based on the situations calculus.