In this paper we present SitLog: a declarative situation-oriented logical language for programming situated service robot tasks. The formalism is task and domain independent, and can be used in a wide variety of settings. SitLog can also be seen as a behaviour engineering specification and interpretation formalism to support action selection by autonomous agents during the execution of complex tasks. The language combines the recursive transition network formalism, extended with functions to express dynamic and contextualized task structures, with a functional language to express control and content information. The SitLog interpreter is written in Prolog and SitLog's programs follow closely the Prolog notation, permitting the declarative specification and direct interpretation of complex applications in a modular and compact form. We discuss the structure and representation of service robot tasks in practical settings and how these can be expressed in SitLog. The present framework has been tested in the service robot Golem-II+ using the specification and programming of the typical tasks which require completion in the RoboCup@Home Competition.

In this paper, we present a concept of service robot and a framework for its functional specification and implementation. The present discussion is grounded in Newell's system levels hierarchy which suggests organizing robotics research in three different layers, corresponding to Marr's computational, algorithmic and implementation levels, as follows: (1) the service robot proper, which is the subject of the present paper, (2) perception and action algorithms, and (3) the systems programming level. The concept of a service robot is articulated in practice through the introduction of a conceptual model for particular service robots; this consists of the specification of a set of basic robotic behaviours and a number of mechanisms for assembling such behaviours during the execution of complex tasks. The model involves an explicit representation of the task structure, allowing for deliberative reasoning and task management. The model also permits distinguishing between a robot's competence and performance, along the lines of Chomsky's corresponding distinction. We illustrate how this model can be realized in practice with two composition modes that we call static and dynamic; these are illustrated with the Restaurant Test and the General Purpose Service Robot Test of the RoboCup@Home competition, respectively. The present framework and methodology has been implemented in the robot Golem-II+, which is also described. The paper is concluded with an overall reflection upon the present concept of a service robot and its associated functional specifications, and the potential impact of such a conceptual model in the study, development and application of service robots in general.

Natural memories are associative, declarative and distributed, and memory retrieval is a constructive operation. In addition, cues of objects that are not contained in the memory are rejected directly. Symbolic computing memories resemble natural memories in their declarative character, and information can be stored and recovered explicitly; however, they are reproductive rather than constructive, and lack the associative and distributed properties. Sub-symbolic memories developed within the connectionist or artificial neural networks paradigm are associative and distributed, but lack the declarative property, the capability of rejecting objects that are not included in the memory, and memory retrieval is also reproductive. In this paper we present a memory model that sustains the five properties of natural memories. We use Relational-Indeterminate Computing to model associative memory registers that hold distributed representations of individual objects. This mode of computing has an intrinsic computing entropy which measures the indeterminacy of representations. This parameter determines the operational characteristics of the memory. Associative registers are embedded in an architecture that maps concrete images expressed in modality specific buffers into abstract representations and vice versa. The framework has been used to model a visual memory holding the representations of hand-written digits. The system has been tested with a set of memory recognition and retrieval experiments with complete and severely occluded images. The results show that there is a range of entropy values, not too low and not too high, in which associative memory registers have a satisfactory performance. The experiments were implemented in a simulation using a standard computer with a GPU, but a parallel architecture may be built where the memory operations would take a very reduced number of computing steps.

Manuscript symbols can be stored, recognized and retrieved from an entropic digital memory that is associative and distributed but yet declarative; memory retrieval is a constructive operation, memory cues to objects not contained in the memory are rejected directly without search, and memory operations can be performed through parallel computations. Manuscript symbols, both letters and numerals, are represented in Associative Memory Registers that have an associated entropy. The memory recognition operation obeys an entropy trade-off between precision and recall, and the entropy level impacts on the quality of the objects recovered through the memory retrieval operation. The present proposal is contrasted in several dimensions with neural networks models of associative memory. We discuss the operational characteristics of the entropic associative memory for retrieving objects with both complete and incomplete information, such as severe occlusions. The experiments reported in this paper add evidence on the potential of this framework for developing practical applications and computational models of natural memory.

The Entropic Associative Memory is a novel declarative and distributed computational model of associative memory. The model is general, conceptually simple, and offers an alternative to models developed within the artificial neural networks paradigm. The memory uses a standard table as its medium, where the information is stored in an indeterminate form, and the entropy plays a functional and operation role. The memory register operation abstracts the input cue with the current memory content and is productive; memory recognition is performed through a logical test; and memory retrieval is constructive. The three operations can be performed in parallel using very few computing resources. In our previous work we explored the auto-associative properties of the memory and performed experiments to store, recognize and retrieve manuscript digits and letters with complete and incomplete cues, and also to recognize and learn phones, with satisfactory results. In such experiments a designated memory register was used to store all the objects of the same class, whereas in the present study we remove such restriction and use a single memory register to store all the objects in the domain. In this novel setting we explore the production of emerging objects and relations, such that cues are used not only to retrieve remembered objects, but also related and imaged objects, and to produce association chains. The present model supports the view that memory and classification are independent functions both conceptually and architecturally. The memory system can store images of the different modalities of perception and action, possibly multimodal, and offers a novel perspective on the imagery debate and computational models of declarative memory.