Lazy Model Expansion: Interleaving Grounding with Search

Finding satisfying assignments for the variables involved in a set of constraints can be cast as a (bounded) model generation problem: search for (bounded) models of a theory in some logic. The state-of-the-art approach for bounded model generation for rich knowledge representation languages, like ASP, FO(.) and Zinc, is ground-and-solve: reduce the theory to a ground or propositional one and apply a search algorithm to the resulting theory. An important bottleneck is the blowup of the size of the theory caused by the reduction phase. Lazily grounding the theory during search is a way to overcome this bottleneck. We present a theoretical framework and an implementation in the context of the FO(.) knowledge representation language. Instead of grounding all parts of a theory, justifications are derived for some parts of it. Given a partial assignment for the grounded part of the theory and valid justifications for the formulas of the non-grounded part, the justifications provide a recipe to construct a complete assignment that satisfies the non-grounded part. When a justification for a particular formula becomes invalid during search, a new one is derived; if that fails, the formula is split in a part to be grounded and a part that can be justified. The theoretical framework captures existing approaches for tackling the grounding bottleneck such as lazy clause generation and grounding-on-the-fly, and presents a generalization of the 2-watched literal scheme. We present an algorithm for lazy model expansion and integrate it in a model generator for FO(ID), a language extending first-order logic with inductive definitions. The algorithm is implemented as part of the state-of-the-art FO(ID) Knowledge-Base System IDP. Experimental results illustrate the power and generality of the approach

A Review of Verbal and Non-Verbal Human-Robot Interactive Communication

In this paper, an overview of human-robot interactive communication is presented, covering verbal as well as non-verbal aspects of human-robot interaction. Following a historical introduction, and motivation towards fluid human-robot communication, ten desiderata are proposed, which provide an organizational axis both of recent as well as of future research on human-robot communication. Then, the ten desiderata are examined in detail, culminating to a unifying discussion, and a forward-looking conclusion

Semantics and symbol grounding in Turing machine processes

The aim of the paper is to present the underlying reason of the unsolved symbolgrounding problem. The Church-Turing Thesis states that a physical problem,for which there is an algorithm of solution, can be solved by a Turingmachine, but machine operations neglect the semantic relationship betweensymbols and their meaning. Symbols are objects that are manipulated on rulesbased on their shapes. The computations are independent of the context, mentalstates, emotions, or feelings. The symbol processing operations are interpretedby the machine in a way quite different from the cognitive processes.Cognitive activities of living organisms and computation differ from each other,because of the way they act in the real word. The result is the problem ofmutual understanding of symbol grounding.The aim of the paper is to present the underlying reason of the unsolved symbolgrounding problem. The Church-Turing Thesis states that a physical problem,for which there is an algorithm of solution, can be solved by a Turingmachine, but machine operations neglect the semantic relationship betweensymbols and their meaning. Symbols are objects that are manipulated on rulesbased on their shapes. The computations are independent of the context, mentalstates, emotions, or feelings. The symbol processing operations are interpretedby the machine in a way quite different from the cognitive processes.Cognitive activities of living organisms and computation differ from each other,because of the way they act in the real word. The result is the problem ofmutual understanding of symbol grounding

What are the key open challenges for understanding the autonomous cumulative learning of skills?

No abstract availabl

Non-Stupidity Condition and Pragmatics in Artificial Intelligence

Symbol Grounding Problem (SGP) (Harnad 1990) is commonly considered one of the central challenges in the philosophy of artificial intelligence as its resolution is deemed necessary for bridging the gap between simple data processing and understanding of meaning and language. SGP has been addressed on numerous occasions with varying results, all resolution attempts having been severely, but for the most part justifiably, restricted by the Zero Semantic Commitment Condition (Taddeo and Floridi 2005). A further condition that demands explanatory power in terms of machine-to-human communication is the Non-Stupidity Condition (Bringsjord 2013) that demands an SG approach to be able to account for plausibility of higher-level language use and understanding, such as pragmatics. In this article, we undertake the endeavour of attempting to explain how merging certain early requirements for SG, such as embodiment, environmental interaction (Ziemke 1998), and compliance with the Z-Condition with symbol emergence (Sun 2000; Tangiuchi et al. 2016, etc.) rather than direct attempts at symbol grounding can help emulate human language acquisition (Vogt 2004; Cowley 2007). Along with the presumption that mind and language are both symbolic (Fodor 1980) and computational (Chomsky 2017), we argue that some rather abstract aspects of language can be logically formalised and finally, that this melange of approaches can yield the explanatory power necessary to satisfy the Non-Stupidity Condition without breaking any previous conditions

Some considerations on the compile-time analysis of constraint logic programs

This paper discusses some issues which arise in the dataflow analysis of constraint logic programming (CLP) languages. The basic technique applied is that of abstract interpretation. First, some types of optimizations possible in a number of CLP systems (including efficient parallelization) are presented and the information that has to be obtained at compile-time in order to be able to implement such optimizations is considered. Two approaches are then proposed and discussed for obtaining this information for a CLP program: one based on an analysis of a CLP metainterpreter using standard Prolog analysis tools, and a second one based on direct analysis of the CLP program. For the second approach an abstract domain which approximates groundness (also referred to as "definiteness") information (i.e. constraint to a single valué) and the related abstraction functions are presented