A Gentle Non-Disjoint Combination of Satisfiability Procedures (Extended Version)

A satisfiability problem is often expressed in a combination of theories, and a natural approach consists in solving the problem by combining the satisfiability procedures available for the component theories. This is the purpose of the combination method introduced by Nelson and Oppen. However, in its initial presentation, the Nelson-Oppen combination method requires the theories to be signature-disjoint and stably infinite (to guarantee the existence of an infinite model). The notion of gentle theory has been introduced in the last few years as one solution to go beyond the restriction of stable infiniteness, but in the case of disjoint theories. In this paper, we adapt the notion of gentle theory to the non-disjoint combination of theories sharing only unary predicates (plus constants and the equality). Like in the disjoint case, combining two theories, one of them being gentle, requires some minor assumptions on the other one. We show that major classes of theories, i.e.\ Löwenheim and Bernays-Schönfinkel-Ramsey, satisfy the appropriate notion of gentleness introduced for this particular non-disjoint combination framework

A Rewriting Approach to the Combination of Data Structures with Bridging Theories

International audienceWe introduce a combination method à la Nelson-Oppen to solve the satisfiability problem modulo a non-disjoint union of theories connected with bridging functions. The combination method is particularly useful to handle verification conditions involving functions defined over inductive data structures. We investigate the problem of determining the data structure theories for which this combination method is sound and complete. Our completeness proof is based on a rewriting approach where the bridging function is defined as a term rewrite system, and the data structure theory is given by a basic congruence relation. Our contribution is to introduce a class of data structure theories that are combinable with a disjoint target theory via an inductively defined bridging function. This class includes the theory of equality, the theory of absolutely free data structures, and all the theories in between. Hence, our non-disjoint combination method applies to many classical data structure theories admitting a rewrite-based satisfiability procedure

Ontology Alignment Evaluation in the Context of Multi-Agent Interactions

Abstract The most prominent way to assess the quality of an ontology alignment is to compute its precision and recall with respect to another alignment taken as reference. These measures determine, respectively, the proportion of found mappings that belong to the reference alignment and the proportion of the reference alignment that was found. The use of these values has been criticised arguing that they fail to reflect important semantic aspects. In addition, they rely on the existence of a reference alignment. In this work we discuss the evaluation of alignments when they are used to facilitate communication between heterogeneous agents. We introduce the notion of pragmatic alignment to refer to the mappings that let agents understand each other, and we propose new versions of precision and recall that measure how useful mappings are for a particular interaction. We then discuss practical applications of these new measures and how they can be estimated dynamically by interacting agents

Politeness and Combination Methods for Theories with Bridging Functions

International audienceThe Nelson-Oppen combination method is ubiquitous in Satisfiability Modulo Theories solvers. However, one of its major drawbacks is to be restricted to disjoint unions of theories. We investigate the problem of extending this combination method to particular non-disjoint unions of theories defined by connecting disjoint theories via bridging functions. A possible application is to solve verification problems expressed in a combination of data structures connected to arithmetic with bridging functions such as the length of lists and the size of trees. We present a sound and complete combination method à la Nelson-Oppen for the theory of absolutely free data structures, including lists and trees. This combination procedure is then refined for standard interpretations. The resulting theory has a nice politeness property, enabling combinations with arbitrary decidable theories of elements. In addition, we have identified a class of polite data structure theories for which the combination method remains sound and complete. This class includes all the subtheories of absolutely free data structures (e.g, the empty theory, injectivity, projection). Again, the politeness property holds for any theory in this class, which can thus be combined with bridging functions and arbitrary decidable theories of elements. This illustrates the significance of politeness in the context of non-disjoint combinations of theories

BMP Signaling Modulates Hepcidin Expression in Zebrafish Embryos Independent of Hemojuvelin

Hemojuvelin (Hjv), a member of the repulsive-guidance molecule (RGM) family, upregulates transcription of the iron regulatory hormone hepcidin by activating the bone morphogenetic protein (BMP) signaling pathway in mammalian cells. Mammalian models have identified furin, neogenin, and matriptase-2 as modifiers of Hjv's function. Using the zebrafish model, we evaluated the effects of hjv and its interacting proteins on hepcidin expression during embryonic development. We found that hjv is strongly expressed in the notochord and somites of the zebrafish embryo and that morpholino knockdown of hjv impaired the development of these structures. Knockdown of hjv or other hjv-related genes, including zebrafish orthologs of furin or neogenin, however, failed to decrease hepcidin expression relative to liver size. In contrast, overexpression of bmp2b or knockdown of matriptase-2 enhanced the intensity and extent of hepcidin expression in zebrafish embryos, but this occurred in an hjv-independent manner. Furthermore, we demonstrated that zebrafish hjv can activate the human hepcidin promoter and enhance BMP responsive gene expression in vitro, but is expressed at low levels in the zebrafish embryonic liver. Taken together, these data support an alternative mechanism for hepcidin regulation during zebrafish embryonic development, which is independent of hjv

Interaction specifications as contexts for ontologies

Common formalisms to represent knowledge, such as Description Logics, are insufficient to represent the dynamic aspects of language when it is used in communication. However, this information can be helpful to describe the semantics of terms in interactions, and in particular to obtain useful alignments that can be used as translations between heterogeneous interlocutors. We propose to consider the specification of interactions as contexts for ontologies. To this aim, the ontology language needs to be extended with a temporal layer that allows to express properties about the possible message exchanges between agents in an interaction. We present a simple preliminary extension for taxonomies, and discuss directions to investigate.This research has been funded by the European Communitys Seventh Framework Programme (FP7/2007-2013) under grant agreement no.567652 /ESSENCE: Evolution of Shared Semantics in Computational EnvironmentPeer Reviewe

A pragmatic approach to translation: vocabulary alignment through multiagent interaction and observation

Tesis llevada a cabo para conseguir el grado de Doctor por la Universidad Autónoma de Barcelona--03-05-2018-Excelente cum laudemEnabling collaboration between agents with different backgrounds is one of the objectives of open and heterogeneous multiagent systems. This can bring together participants with different knowledge, abilities, and access to resources. For this collaboration to succeed, it needs to deal with different kinds of heterogeneity that can exist between agents. An important aspect of this heterogeneity is the linguistic one. To coordinate their collaborative actions, agents need to communicate with each other; and to ensure meaningful communication it is essential that they use the same vocabulary (and understand it in the same way). The problem of achieving common understanding between agents that use different vocabularies has been mainly addressed by techniques that assume the existence of shared external elements, such as a meta-language, a physical environment, or semantic resources. These elements are not always available and, even when they are, they may yield alignments that are not useful for the particular type of interactions agents need to perform, as they are not contextualized. In this dissertation we investigate a different approach to vocabulary alignment. We consider agents that only share knowledge of how to perform a task, given by the specification of an interaction protocol. We study the idea of interaction-based vocabulary alignment, a framework that lets agents learn a vocabulary alignment from the experience of interacting; by observing what works and what does not in a conversation. To give an intuition, consider someone trying to order a coffee in a foreign country. Even if there is no common language, the interaction is likely to succeed, since it consists of simple, well-understood steps that interlocutors agree on. Moreover, it is likely that, if our subject repeats the ordering coffee interaction many times, she will end up learning how it is performed in the foreign language. While humans are very good at adapting in this way, this idea has not been explored in depth for the case of artificial agents. Throughout this dissertation we study how agents can learn a new vocabulary when they follow specifications that use different formalizations. Concretely, we consider interaction-based vocabulary alignment for protocols specified with finite state machines, with logical constraints, and with a social semantics based on commitments. For each case, we provide techniques to infer semantic information from interacting, or observing interactions between other agents. We also analyze how these techniques can be used in combination with external alignments obtained in a different way. When these alignments are not necessarily correct, our techniques provide ways of repairing them. For each type of specification we evaluate the proposed methods by simulating their use in a set of artificial, randomly generated protocols. This provides a general evaluation that does not suffer the biases of particular datasets. Later, we apply our methods to an empirical dataset of human-crafted instructional protocols, obtained from the WikiHow webpage. We discuss the challenges of using our methods in protocols with natural language labels, and we show how the resulting method improves on the performance of using a well-known dictionary. Summarizing, we present a vocabulary alignment method that is context-specific, lightweight, cheap and independent of external resources. This method can be used by agents as a low profile method of learning the vocabulary used in particular situations. Our method allows agents to find a useful alignment, although slowly. In combination with other resources, our technique provides not only a way of learning alignments faster, but also a way of obtaining different information (about the use of words in context) that may be difficult to find otherwise, and to repair external alignmentsPeer reviewe

Vocabulary alignment for collaborative agents: a study with real-world multilingual how-to instructions

Collaboration between heterogeneous agents typically requires the ability to communicate meaningfully. This can be challenging in open environments where participants may use different languages. Previous work proposed a technique to infer alignments between different vocabularies that uses only information about the tasks being executed, without any external resource. Until now, this approach has only been evaluated with artificially created data. We adapt this technique to protocols written by humans in natural language, which we extract from instructional webpages. In doing so, we show how to take into account challenges that arise when working with natural language labels. The quality of the alignments obtained with our technique is evaluated in terms of their effectiveness in enabling successful collaborations, using a translation dictionary as a baseline. We show how our technique outperforms the dictionary when used to interact.</p

Attuning ontology alignments to semantically heterogeneous multi-agent interactions

In this paper we tackle the problem of semantic heterogeneity in multi-agent communication, i.e., when agents in a multi-agent system use different vocabularies for message passing, or might interpret shared vocabulary in varying ways. The problem of achieving meaningful communication in such semantically heterogeneous multi-agent interactions has been mainly tackled either by using ontology alignments to translate vocabularies, or by using methods that learn an alignment by observing how the utterance of particular terms affects the unfolding of an interaction. We propose solutions that combine these approaches and study how agents can use external alignments with possibly incomplete or erroneous mappings when communicating with each other in the context of a multi-agent interaction. We further show experimentally that with the experience gained through repeated interactions and by using simple learning techniques agents can find and repair those mappings of an ontology alignment that lead to unsuccessful interactions, thus improving the success rate of their future interactions. © 2016 The Authors and IOS Press.This research has been funded by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agree- ment no. 567652 /ESSENCE: Evolution of Shared Semantics in Computational Environments.Peer Reviewe