Understanding the trade-offs available in the design space of intelligent systems is a major unaddressed element in the study of Artificial Intelligence. In this paper we approach this problem in two ways. First, we discuss the development of our integrated robotic system in terms of its trajectory through design space. Second, we demonstrate the practical implications of architectural design decisions by using this system as an experimental platform for comparing behaviourally similar yet architecturally different systems. The results of this show that our system occupies a sweet spot in design space in terms of the cost of moving information between processing components.

Flexible general purpose robots need to tailor their visual processing to their task, on the fly. We propose a new approach to this within a planning framework, where the goal is to plan a sequence of visual operators to apply to the regions of interest (ROIs) in a scene. We pose the visual processing problem as a Partially Observable Markov Decision Process (POMDP). This requires probabilistic models of operator effects to quantitatively capture the unreliability of the processing actions, and thus reason precisely about trade-offs between plan execution time and plan reliability. Since planning in practical sized POMDPs is intractable we show how to ameliorate this intractability somewhat for our domain by defining a hierarchical POMDP. We compare the hierarchical POMDP approach with a Continual Planning (CP) approach. On a real robot visual domain, we show empirically that all the planning methods outperform naive application of all visual operators. The key result is that the POMDP methods produce more robust plans than either naive visual processing or the CP approach. In summary, we believe that visual processing problems represent a challenging and worthwhile domain for planning techniques, and that our hierarchical POMDP based approach to them opens up a promising new line of research.

Operating in a physical context, an intelligent robot faces two fundamental problems. First, it needs to combine information from its different sensors to form a representation of the environment that is more complete than any representation a single sensor could provide. Second, it needs to combine high-level representations (such as those for planning and dialogue) with sensory information, to ensure that the interpretations of these symbolic representations are grounded in the situated context. Previous approaches to this problem have used techniques such as (low-level) information fusion, ontological reasoning, and (high-level) concept learning. This paper presents a framework in which these, and related approaches, can be used to form a shared representation of the current state of the robot in relation to its environment and other agents. Preliminary results from an implemented system are presented to illustrate how the framework supports behaviours commonly required of an intelligent robot.

In this paper we propose a method for continuous learning of simple visual concepts. The method continuously associates words describing observed scenes with automatically extracted visual features. Since in our setting every sample is labelled with multiple concept labels, and there are no negative examples, reconstructive representations of the incoming data are used. The associated features are modelled with kernel density probability distribution estimates, which are built incrementally. The proposed approach is applied to the learning of object properties and spatial relations.

In this paper we propose an empirical method for the comparison of architectures designed to produce similar behaviour from an intelligent system. The approach is based on the exploration of design space using similar designs that all satisfy the same requirements in niche space. An example of a possible application of this method is given using a robotic system that has been implemented using a software toolkit that has been designed to support architectural experimentation.

We present integration mechanisms for combining heterogeneous components in a situated information processing system, illustrated by a cognitive robot able to collaborate with a human and display some understanding of its surroundings. These mechanisms include an architectural schema that encourages parallel and incremental information processing, and a method for binding information from distinct representations that when faced with rapid change in the world can maintain a coherent, though distributed, view of it. Provisional results are demonstrated in a robot combining vision, manipulation, language, planning and reasoning capabilities interacting with a human and manipulable objects.

We present an artificial cognitive system for learning visual concepts. It comprises of vision, communication and manipulation sub- systems, which provide visual input, enable verbal and non-verbal com- munication with a tutor and allow interaction with a given scene. The main goal is to learn associations between automatically extracted visual features and words that describe the scene in an open-ended, continuous manner. In particular, we address the problem of cross-modal learning of visual properties and spatial relations. We introduce and analyse several learning modes requiring different levels of tutor supervision.

In human-robot interaction (HRI) it is essential that the robot interprets and reacts to a human's utterances in a manner that reflects their intended meaning. In this paper we present a collection of novel techniques that allow a robot to interpret and execute spoken commands describing manipulation goals involving qualitative spatial constraints (e.g. ``put the red ball near the blue cube''). The resulting implemented system integrates computer vision, potential field models of spatial relationships, and action planning to mediate between the continuous real world, and discrete, qualitative representations used for symbolic reasoning.

Human-Robot Interaction (HRI) invariably involves dialogue about objects in the environment in which the agents are situated. The paper focuses on the issue of resolving discourse references to such visual objects. The paper addresses the problem using strategies for intra-modal fusion (identifying that different occurrences concern the same object), and inter-modal fusion, (relating object references across different modalities). Core to these strategies are sensorimotoric coordination, and ontology-based mediation between content in differentmodalities. The approach has been fully implemented, and is illustrated with several working examples.