Symbol Grounding

During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [35]) adopt a classical symbolic approach, some (e.g. LEABRA[48]) are based on a purely con-nectionist model, while others (e.g. CLARION [59]) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are also available [34]. In this paper we propose a reflection on the role that Conceptual Spaces, a framework developed by Peter Gärdenfors [24] more than fifteen years ago, can play in the current development of the Knowledge Level in Cognitive Systems and Architectures. In particular, we claim that Conceptual Spaces offer a lingua franca that allows to unify and generalize many aspects of the symbolic, sub-symbolic and diagrammatic approaches (by overcoming some of their typical problems) and to integrate them on a common ground. In doing so we extend and detail some of the arguments explored by Gärdenfors [23] for defending the need of a conceptual, intermediate, representation level between the symbolic and the sub-symbolic one. In particular we focus on the advantages offered by Conceptual Spaces (w.r.t. symbolic and sub-symbolic approaches) in dealing with the problem of compositionality of representations based on typicality traits. Additionally, we argue that Conceptual Spaces could offer a unifying framework for interpreting many kinds of diagrammatic and analogical representations. As a consequence, their adoption could also favor the integration of diagrammatical representation and reasoning in CAs.

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.

The Chinese room argument has presented a persistent headache in the search for Artificial Intelligence. Since it first appeared in the literature, various interpretations have been made, attempting to understand the problems posed by this thought experiment. Throughout all this time, some researchers in the Artificial Intelligence community have seen Symbol Grounding as proposed by Harnad as a solution to the Chinese Room Argument. The main thesis in this paper is that although related, these two issues present different problems in the framework presented by Harnad himself. The work presented here attempts to shed some light on the relationship between Searle’s intentionality notion and Harnad’s Symbol Grounding Problem.

Abstract— In this paper, a robot learning approach is proposed which integrates Visuospatial Skill Learning, Imitation Learning, and conventional planning methods. In our approach, the sensorimotor skills (i.e., actions) are learned through a learning from demonstration strategy. The sequence of performed actions is learned through demonstrations using Visuospatial Skill Learning. A standard action-level planner is used to represent a symbolic description of the skill, which allows the system to represent the skill in a discrete, symbolic form. The Visuospatial Skill Learning module identifies the underlying constraints of the task and extracts symbolic predicates (i.e., action preconditions and effects), thereby updating the planner representation while the skills are being learned. Therefore the planner maintains a generalized representation of each skill as a reusable action, which can be planned and performed independently during the learning phase. Preliminary experimental results on the iCub robot are presented.

This article is the second step in our research into the Symbol Grounding Problem (SGP). In a previous work, we defined the main condition that must be satisfied by any strategy in order to provide a valid solution to the SGP, namely the zero semantic commitment condition (Z condition). We then showed that all the main strategies proposed so far fail to satisfy the Z condition, although they provide several important lessons to be followed by any new proposal. Here, we develop a new solution of the SGP. It is called praxical in order to stress the key role played by the interactions between the agents and their environment. It is based on a new theory of meaning—Action-based Semantics (AbS)—and on a new kind of artificial agents, called two-machine artificial agents (AM2). Thanks to their architecture, AM2s implement AbS, and this allows them to ground their symbols semantically and to develop some fairly advanced semantic abilities, including the development of semantically grounded communication and the elaboration of representations, while still respecting the Z condition.

"Although rudimentary memory systems for autonomous agents have existed for quite a while, human-like memory systems which contain extensive episodic, social and affective components, and which can be utilized in order to maintain relationships with humans, have only recently started to appear. Interactive robots are a special case of an autonomous agent, which is physically situated, interacts with humans, and which has sensing and motor abilities. Furthermore, social robots might have regular encounters and interactions with other human or non-human entities, and might maintain relationships with
them.
In this paper, a set of desiderate for memory systems of companions will be presented, together with case studies of the memory subsystems of two existing robotic systems: first, Ripley the Robot, an interactive manipulator robot arm withvision, dialogue, and a sensorymotor-grounded situation model; and second, Sarah the FaceBot, a social mobile robot with face recognition, dialogue, as well as a social and interaction database which is bi-directionally connected to the FaceBook website.
The case studies provide the basis for an interesting discussion involving physical situations, self-models, expectations, and most importantly episodic and social memories, human- or machine- authored, internal or external, private or shared."