The complex process of deriving programs from specifications is often divided into the following three steps: (i) the derivation of formal specifications from the informal ones; (ii) the validation of the formal specifications; and (iii) the derivation of executable programs from the formal specifications.

The definitions above, separated by ten years, represent two very different conceptions of learning. For Simon learning depends on an internal change in representation, and for Kaelbling it is instead measured in terms of an external change in behaviour. Furthermore, Kaelbling's focus on the situatedness of the learning system being embedded in its environment reflects the recent experience gained by much direct experimentation with physical robots.

In recent years, the capabilities of knowledge-based systems to communicate with their users have evolved from simple interactions to complex dialogues. With this evolution comes a need to understand what makes a good dialogue. In this paper, we are concerned with dialogue coherence. We review the notion of focus, which partly explains this property, and its use for user-system communication. First, we examine the major theories dealing with this notion. We describe what their contribution is and how they differ. Then, we illustrate the benefits of using the notion of focus and especially the improvement in text coherence. We pay particular attention to how the notion can concretely be implemented. Its integration with other techniques and theories is described. We conclude the paper by pointing out remaining issues in the understanding of the notion of focus. The contribution of this paper is to provide a classification of the theories of focus and to show the improvements they offer in elaborate user-system dialogues.

Ontologies provide potential support for knowledge and content management on a P2P platform. Although we can design ontologies beforehand for an application, it is argued that in P2P environments static or predefined ontologies cannot satisfy the ever-changing requirements of all users. So we propose every user should make proposals for what kind of ontology is the most apt to his need. Collecting all these proposals (or votes) helps the drift of ontologies. This paper introduces OntoVote, a scalable distributed vote-collecting mechanism based on application-level broadcast trees, and describes how OntoVote can be applied to ontology drift on a P2P platform by discussing several problems involved in the voting process.

In this paper, we discuss the challenges posed by the NEUROWEB project, as a case study of ontological modeling at a knowledge interface between neurovascular medicine and genomics. The aim of the project is the development of a support system for association studies. We identify the notion of clinical phenotypes, that is, the pathological condition of a patient, as the central construct of the knowledge model. Clinical phenotypes are assessed through the diagnostic activity, performed by clinical experts operating within communities of practice; the different communities operate according to specific procedures, but they also conform to the minimal requirements of international guidelines, displayed by the adoption of a common standard for the patient classification. We develop a central model for the clinical phenotypes, able to reconcile the different methodologies into a common classificatory system. To bridge neurovascular medicine and genomics, we identify the general theory of biological function as the common ground between the two disciplines; therefore, we decompose the clinical phenotypes into elementary phenotypes with a homogeneous physiological background, and we connect them to the biological processes, acting as the elementary units of the genomic world.

Patterns have been extensively used to extract hypernym relations from texts. The most popular patterns are Hearst’s patterns, formulated as regular expressions mainly based on lexical information. Experiences have reported good precision and low recall for such patterns. Thus, several approaches have been developed for improving recall. While these approaches perform better in terms of recall, it remains quite difficult to further increase recall without degrading precision. In this paper, we propose a novel 3-phase approach based on sequential pattern mining to improve pattern-based approaches in terms of both precision and recall by (i) using a rich pattern representation based on grammatical dependencies (ii) discovering new hypernym patterns, and (iii) extending hypernym patterns with anti-hypernym patterns to prune wrong extracted hypernym relations. The results obtained by performing experiments on three corpora confirm that using our approach, we are able to learn sequential patterns and combine them to outperform existing hypernym patterns in terms of precision and recall. The comparison to unsupervised distributional baselines for hypernym detection shows that, as expected, our approach yields much better performance. When compared to supervised distributional baselines for hypernym detection, our approach can be shown to be complementary and much less loosely coupled with training datasets and corpora.

This work studies quantitative measures for ranking judicial decisions by the Brazilian Supreme Court [Supremo Tribunal Federal (STF)] and selecting leading cases, which are understood as those with broadness of influence on different legal fields. The measures are based on a network built over decisions whose cases were finalized in the Brazilian Supreme Court between 01/2001 and 12/2019, which were obtained by crawling publicly available STF records. Three ranking measures are proposed; two are adaptations of the PageRank algorithm, and one adapts Kleinberg’s algorithm. Such measures are compared with respect to agreement on top 100 rankings; we also analyze each robustness measure based on self-agreement under perturbation.

We examine whether the resulting quantitative ranking is congenial to a qualitative intuition of what the legal community usually considers as relevant precedents. We also discuss some possible criteria of relevance in the seek for patterns that suggest how quantitative and qualitative measures would better align. The ranking of leading cases and relevant decisions improved after building decision networks without irrelevant appeals and decisions that overflow the court offers a starting point to discuss the role of STF in the Brazilian judicial system.

In our last work, both versions of PageRank and Kleinberg algorithms produced different rankings and all of them were robust with respect to 10% and 20%-perturbation levels, but none of them retrieved leading cases at the top of these rankings. Then, we took a further step in the studies of the STF decision network and we introduced better filtering of network nodes guided by legal expertise on the works of the Supreme Court. We also introduced more fine-grained perturbance levels to understand the impact of such filters in the STF decision network. We concluded that after filtering low-relevance decision types, the STF decision network is still robust under 10%-perturbation, but it presents higher degradation by increasing perturbation levels. The two versions of PageRank still produce different rankings. Kleinberg’s algorithm provides a different ranking, with many relevant criminal cases. Although we improved algorithms rankings filtering decisions from the network, which represents an important methodological step, there is still room for improvement. Given that relevant decisions are well ranked after filtering out a large amount of irrelevant decisions, the results set a starting point to discuss the role of STF in the Brazilian judicial system.

Visuo is an implemented Python program that models visual reasoning. It takes as input a description of a scene in words (e.g. ‘small dog on a sunny street’) and produces estimates of the quantitative magnitudes of the qualitative input (e.g. the size of the dog and the brightness of the street). We claim that reasoners transfer quantitative knowledge to new concepts from distributions of familiar concepts in memory. We also claim that visuospatial magnitudes should be stored as distributions over fuzzy sets. We show that Visuo successfully predicts quantitative knowledge to new concepts.

This paper discusses a goal-based approach for the engineering of capacity-driven Web services. In this approach, goals are established to first, define the roles that these Web services will play in implementing business applications, second, frame the requirements that will be put on these Web services, and third, identify the processes in terms of business logics that these Web services will carry out. Because of the nature of capacity-driven Web services compared with regular (i.e. mono-capacity) Web services, their engineering in terms of design, development, and deployment takes place in a different way. A Web service that is empowered with several capacities, which are basically separate groups of operations to execute, has to choose one capacity for triggering at run-time. To this end, the Web service takes into account different types of requirements like data and privacy that are put on each capacity that empowers this Web service.

Learning automata are reinforcement learners belonging to the class of policy iterators. They have already been shown to exhibit nice convergence properties in a wide range of discrete action game settings. Recently, a new formulation for a continuous action reinforcement learning automata (CARLA) was proposed. In this paper, we study the behavior of these CARLA in continuous action games and propose a novel method for coordinated exploration of the joint-action space. Our method allows a team of independent learners, using CARLA, to find the optimal joint action in common interest settings. We first show that independent agents using CARLA will converge to a local optimum of the continuous action game. We then introduce a method for coordinated exploration which allows the team of agents to find the global optimum of the game. We validate our approach in a number of experiments.

Solving a legal problem is a complex business. First, the lawyer—or whoever is doing the solving—has to assemble and marshall the facts of his case in as orderly a fashion as his knowledge of the relevant law will allow. Secondly, he must confront the range of pertinent legal sources and decompose these into manageable and applicable units. Thirdly, he will subsume the particular facts of his case under the more general terms of his decomposed units. And, finally, he draws inferences about the applicable legal provisions and their impact on the facts. Of course, assembly, decomposition, subsumption and inference may not occur in linear progression. What is more, problems can cccur at each stage and interim evaluations and judgments must be made.

Usually, humanoid walking gaits are only roughly distinguished between stable and unstable. The evaluation of a stable humanoid walking gait is difficult to quantify in scales. And, it is extremely hard to adjust humanoid robots in suitable a walking gait for different movement objectives such as fast walking, uneven floor walking, and so on. This paper proposes a stability margin constructed by center of pressure (COP) to evaluate the gait stability of humanoid walking. The stability margin is modeled by the COP regions that a humanoid robot needs for stable standing. We derive the mathematical model for COP position by dividing the walking gait into single and double support phases in order to measure the stability of the COP regions. An actual measuring system for the stable COP regions is designed and implemented. The measured COP trajectory of a walking gait is eventually evaluated with respect to the stable COP regions for the stability margins. The evaluation focuses on weak stability areas to be improved for robust walking gaits. To demonstrate the robustness of the improved walking gait, we replicate the experiment on three different terrains. The experiments demonstrate that the walking gaits developed based on stable COP region can be applied for different movement objectives.

We propose a logic-based framework to model a system whose aim is to help provide the user with those pieces of information that are useful with respect to his/her current information need, as well as relevant to his/her query. More precisely, we propose three measures of information usefulness which take into account the fact that the user can be represented as a cognitive agent endowed with some beliefs—a partial “picture” about what it already knows—and goals—a certain state of affairs in which the agent would like to be. This paper extends a previous version of the framework by considering a more realistic hypothesis, according to which there are several ways to achieve goals. We present three different approaches: the binary approach, the ordinal approach, and the numerical approach. We take information retrieval (IR) as a particular application domain, and we compare some existing measures with the usefulness measure we introduce here.

The preceding paper on the refinement of rule-based systems by Precce et al. reflects a recent, rapid growth of interest in verification and validation of knowledge based systems. Much of the literature on this subject is thinly spread over conference proceedings, professional periodicals and academic journals, so we have tried to collect together some of the more frequently cited work to assist readers interested in the area.

James Albus states that “an architecture is a description of how a system is constructed from basic components and how those components fit together to form the whole” (Albus, 1995). A software architecture for physical agents reflects the organising principles that its designers have learned from many prior experiences in building such agents. Architectures that have been proposed for physical agents have differed greatly—from subsumption (Brooks, 1986) to Soar (Laird et al., 1987). However, a surprising consensus about architectures is beginning to emerge within the small community of researchers applying artificial intelligence to robotics. The consensus is that a multi- layer, hierarchical architecture is necessary. In particular, the community is moving towards a three-layered architecture. The lowest layer is a reactive control system inspired by subsumption (Brooks, 1986). The top layer is a traditional symbolic planning and modelling system. The middle layer is the key; it serves as a “differential” between the short-range reaction and long-range reasoning.

The knowledge engineering practices of one experienced company are outlined, commenting on differences in emphasis in some of the knowledge elicitation and AI research literatures.

Finance is a challenging yet appropriate domain for model-based reasoning, an area of research otherwise grounded in classical physics. Among the many features that suggest a model-based approach are that firms have formal internal structures, business entities have idealizable behaviours, and there is a history of formal analysis of business problems. This article discusses the motivations and foundations of the model-based approach, and surveys several existing artificial intelligence programs that exploit its advantages. The survey shows that there are ample opportunities for useful systems and significant research in this area. However, accomplishing either of these goals depends crucially upon moving beyond qualitative models based only on accounting information, which tend not to capture the actual complexities of the domain.

The field of humanoid robotics is constantly evolving, with new advances creating exciting opportunities for research and development. Especially in the entertainment area, robotics applications show significant growth potential. To guide and track the progress of robotics research, good benchmark problems are crucial, but especially investigating human–robot interaction capabilities is difficult. This paper examines robot magic shows as a benchmark and research direction for entertainer robots and discusses flexible and versatile system architectures for robot magicians in the humanoid robot application challenge competition since 2017. The goal is a detailed analysis of magic tricks and magician robots presented in front of audiences. This paper reviews the hardware components and examines the robot platforms of participating teams in the robot magic show, the types of magic performances, the magic tricks and tools, the algorithms for the magic tricks, and the framework for humanoid robots. By providing a comprehensive analysis of these elements, we can gain insight into the capabilities of advanced humanoid robots for high-performance magic tricks. In conclusion, this paper highlights the exciting potential of humanoid robotics and entertainment fusion. In addition, we analyze the use of humanoid robots in magic shows, presenting the industrial potential of entertainer robots.