Automated reasoning

Software radios are emerging as platforms for multiband multimode personal communications systems. Radio etiquette is the set of RF bands, air interfaces, protocols, and spatial and temporal patterns that moderate the use of the radio spectrum. Cognitive radio extends the software radio with radio-domain model-based reasoning about such etiquettes. Cognitive radio enhances the flexibility of personal services through a Radio Knowledge Representation Language. This language represents knowledge of radio etiquette, devices, software modules, propagation, networks, user needs, and application scenarios in a way that supports automated reasoning about the needs of the user. This empowers software radios to conduct expressive negotiations among peers about the use of radio spectrum across fluents of space, time, and user context. With RKRL, cognitive radio agents may actively manipulate the protocol stack to adapt known etiquettes to better satisfy the user's needs. This transforms radio nodes from blind executors of predefined protocols to radio-domain-aware intelligent agents that search out ways to deliver the services the user wants even if that user does not know how to obtain them. Software radio [1] provides an ideal platform for the realization of cognitive radio.

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.

Automating systems can assist individuals making administrative decisions or help identify relevant criteria, evidence or particular issues for consideration. Automation has the potential to make decision-making more accurate, consistent, cost-effective, timely and diminish the risk the decision will be invalidated due to improper motivations or bad faith. However, automated systems raise difficult questions about authorisation and reviewability of decision-making by a non-human agent; there are difficulties applying legislation and resolving complexities and ambiguity; and potential for coding errors. There is still potential for jurisdictional error if there is an error in the automation. This is illustrated by the difficulties associated with the Commonwealth Department of Human Services and Centrelink’s online system for raising and recovering social security debts. Machine learning through “Big Data” is a branch of Artificial Intelligence that allows computer systems to learn directly from examples, data and experience. The article analyses the Commonwealth, NSW and South Australian data-sharing legislation and the need for protection for vulnerable people, such as children or people with a mental illness, transparency and accountability.

The word hydroponics has its derivation from the combination of two Greek words, hydro meaning water and ponos meaning labour, i.e., working water. Hydroponics used to be considered a system where there was no growing media at all, such as the nutrient film technique in vegetables. But today it's accepted that a soil-less growing medium is often used to support the plant root system physically and provide for a favourable buffer of solution around the root system. The system that is used will be automated to prevent many disadvantages that we have in a manual system.

In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense cat-egorization (prototypical and exemplars-based categorization) with standard monotonic cate-gorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual structures represented as heterogeneous proxytypes. Dual-PECCS has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies, and its output has been compared to human responses. The obtained results suggest that our approach can be beneficial to improve the representational and reasoning conceptual capabilities of standard cognitive artificial systems, and –in addition– that it may be plausibly applied to different general computational models of cognition. The current version of the system , in fact, extends our previous work, in that Dual-PECCS is now integrated and tested into two cognitive architectures, ACT-R and CLARION, implementing different assumptions on the underlying invariant structures governing human cognition. Such integration allowed us to extend our previous evaluation.

Ibn Tufayl's Hayy Ibn Yaqzan is a remarkably creative piece of writing that came down to us from the middle ages. It has been considered a classic of medieval Islamic philosophy, and a classic of Arabic novels that had a great influence on the medieval and renaissance west.
The theme of East and West is given prominence in this paper. The first part of my argument concerns the ideological and cultural coloring of the context in which this novel was written. The second part deals with the point of intersection of contesting ideologies in the novel. It also deals with the astonishing variety of conclusions the novel has yielded.
In estimating the novel as both philosophical literature and literary philosophy, which as I argue, cannot be studied in isolation from the famous translation movement (from Greek into Arabic in the middle ages), I would not exclude another possibility that Ibn Tufayl benefited from stoicism. This presupposes Ibn Tufayl's acquaintance with the stoics.
There are also similarities between Hayy Ibn Yaqzan and the cosmogony of the first book of Ovid's Metamorphoses, in relation to his account of the creation and early history of man, which was influenced by the Epicurean Roman poet Lucretius.
I would suggest that there might have been a common Greek source, read by Lucretius, Ovid and Ibn Tufayl (in translation in the last case).
Although, in a way, Hayy Ibn Yaqzan was a direct consequence of Greek thought translated into Arabic, Ibn Tufayl, however, pursued his own Islamic way of reconciling philosophy with religion.

This theoretical article aims to draw up an inventory of the latest advances in medical knowledge engineering in the specific area of ontologies and knowledge based systems design. Echoing the debates that animated the landscape of Artificial Intelligence (AI) from the 1970s under the impetus of Dreyfus HL, it aims to show that most of the difficulties currently faced by medical knowledge engineering are inherent in the nature of AI, whose project is the mechanization of cognitive activity. As such it promotes the idea that only a fair understanding of what machines can do, given their machinic character itself, and remains, despite its cognitive finitude, a property of human being, may offer to balancing the scales between tasks that can be allocated to machines and those that have to be left in charge of humans.

We discuss mechanised proofs of Fermat's Little Theorem in a variety of styles, focusing in particular on an elegant combinatorial "necklace" proof that has not been mechanised previously. What is elegant in prose turns out to be long-winded mechanically, and so we examine the effect of explicitly appealing to group theory. This has pleasant consequences both for the necklace proof, and also for some of the direct number-theoretic approaches.

This paper presents a research in progress about the French ISyCri project that aims at providing partners involved in crisis management with an agile Mediation Information System (MIS). Not only this MIS shoul support the interoperability of the partners' information systems but it is also dedicated to coordinate their activities through a collaborative process. One of the first and main steps towards such a MIS, is to elaborate a common and sharable reference model built to characterize crisis situations. Such a model is also an input for automated reasoning to elaborate and adapt a crisis solving collaborative process. This article presents the objective of the project, our approach and our first results: a UML metamodel of crisis situation and its corresponding OWL ontology on top of which deductions are possible.

The idea that science can be automated is so deeply related to the view that the method of mathematics is the axiomatic method, that confuting the claim that mathematical knowledge can be extended by means of the axiomatic method is almost equivalent to confuting the claim that science can be automated. I argue that the axiomatic view is inadequate as a view of the method of mathematics and that the analytic view is to be preferred. But, if the method of mathematics and natural sciences is the analytic method, then the advancement of knowledge cannot be mechanized, since non-deductive reasoning plays a crucial role in the analytic method, and non-deductive reasoning cannot be fully mechanized.

Recent years have seen rapid progress in the development of ontologies as semantic models intended to capture and represent aspects of the real world. There is, however, great variation in the quality of ontologies. If ontologies are to become progressively better in the future, more rigorously developed, and more appropriately compared, then a systematic discipline of ontology evaluation must be created to ensure quality of content and methodology. Systematic methods for ontology evaluation will take into account ...

As applications of description logics proliferate, efficient reasoning with knowledge bases containing many assertions becomes ever more important. For such cases, we developed a novel reasoning algorithm that reduces a \(\mathcal{SHIQ}\) knowledge base to a disjunctive datalog program while preserving the set of ground consequences. Queries can then be answered in the resulting program while reusing existing and practically proven optimization techniques of deductive databases, such as join-order optimizations or magic sets. Moreover, we use our algorithm to derive precise data complexity bounds: we show that \(\mathcal{SHIQ}\) is data complete for NP, and we identify an expressive fragment of \(\mathcal{SHIQ}\) with polynomial data complexity.

The design of safe industrial controllers is one of the most important domains related to Automation Systems research. To support it, synthesis and analysis techniques are available. Among the analysis techniques, two of the most important are Simulation and Formal Verification. In this paper these two techniques are used together in a complementary way. Understanding plant behaviour is essential for obtaining safe industrial systems controllers; hence, plant modelling is crucial to the success of these techniques. A two step approach is presented: first, the use of Simulation and, second, the use of Formal Verification of Industrial Systems Specifications. The specification and plant models used for each technique are described. Simulation and Formal Verification results are presented and discussed. The approach presented in the paper can be applied to real industrial systems, and obtain safe controllers for hybrid plants. The Modelica modelling language and Dymola simulation environment are used for Simulation purposes, and Timed Automata formalism and the UPPAAL real-time model-checker are used for Formal Verification purposes.

Any theory that tries to describe human reasoning with one single-layered theory is doomed to be incomplete. In this chapter we argue that the understanding of human reasoning requires a multi-layered approach, where every layer is guided by at least one process. A full account of the human reasoning system will incorporate a multi-phase experience and context driven application of top-down driven reasoning principles and tactics operating on bottom-up knowledge and principles, acquired from everyday interactions. This chapter aims to advance this more comprehensive approach to the human reasoning system.

In response to a paper by Harris & Fitelson (2001), Slaney (2002) states several open questions concerning possible strategies for proving distributivity in a wide class of positive sentential logics.  In this note, I provide  answers to all of Slaney's open questions.  The result is a better understanding of the class of positive logics in which distributivity holds.

A syllogism, also known as a rule of inference, is a formal logical scheme used to draw a conclusion from a set of premises. In a categorical syllogisms, every premise and conclusion is given in form a of quantified relationship between two objects. The syllogistic system consists of systematically combined premises and conclusions to so called figures and moods. The syllogistic system is a theory for reasoning, developed by Aristotle, who is known as one of the most important contributors of the western thought and logic. Since Aristotle, philosophers and sociologists have successfully modelled human thought and reasoning with syllogistic structures. However, a major lack was that the mathematical properties of the whole syllogistic system could not be fully revealed by now. To be able to calculate any syllogistic property exactly, by using a single algorithm, could indeed facilitate modelling possibly any sort of consistent, inconsistent or approximate human reasoning. In this paper we present such an algorithm.

Till today chilli seeds are separated manually and there is no specific machine designed to separate seed from its pulp. The manual process is carried out by hammering chilli fruits with wooden stick, separating the husk using hand separator. This manual method is quite tiresome mainly due to the inhalation of fine particles of chilli fruits by labours. Its pungency results in continuous sneezing and irritation for labours. Hence, it is necessary to eliminate laborious work and time consumption. The automated process of chilli seed extraction will be the better solution for this problem, which helps farmers. This automated method can also be adopted by food industries for large scale extraction of chilli seed, as well.

The relation between knowledge and action is discussed. It is argued that knowledge comprises static as well as dynamic aspects that are related to expectation of success of possible action and control over the performance of factual action, respectively. Both views concur in success of action as common point of reference. The main claim of this paper is that regarding this reference to action propositional knowledge does not diiffer from practical knowledge. In this way action establishes a natural dependency of knowledge on context. The approach is compared to the analytical characterization of propositional knowledge as justified true belief, pointing at existing connections. Consequences for knowledge management are indicated regarding the support of knowledge intensive work and knowledge transfer.

— In this paper a scalable and flexible Architecture for real-time mission planning and dynamic agent-to-task assignment for a swarm of Unmanned Aerial Vehicles (UAV) is presented. The proposed mission planning architecture consists of a Global Mission Planner (GMP) which is responsible of assigning and monitoring different high-level missions through an Agent Mission Planner (AMP), which is in charge of providing and monitoring each task of the mission to each UAV in the swarm. The objective of the proposed architecture is to carry out high-level missions such as autonomous multi-agent exploration, automatic target detection and recognition, search and rescue, and other different missions with the ability of dynamically re-adapt the mission in real-time. The proposed architecture has been evaluated in simulation and real indoor flights demonstrating its robustness in different scenarios and its flexibility for real-time mission re-planning and dynamic agent-to-task assignment.

Feature models have been cited as one of the main contributions to model software product families. However, there is still a gap in product family engineering which is the automated reasoning on feature models. In this paper we describe how to reason on feature models using constraint programming. Although, there are a few attempts to reason on feature models there are two main drawbacks in these proposals: µ none of them associate parameters to features µ none of them use constraint programming as the reasoning base. Using constraint programming endows our proposal with a more powerful reasoning capacity and greater expressiveness than others.

Business Continuity Management (BCM) is a process to manage risks, emergencies, and recovery plans of an organization during a crisis. It results in a document called Business Continuity Plans (BCP) that specifies the methodology and procedures required to backup and recover the functional unit of a disrupted business. Traditionally, the BCP assessment is based only on the continuity of IS infrastructures and does not consider possible relations with the business objectives and business processes. This traditional approach assumes that the risk of business continuity is resulted from the disruption of the IS infrastructures. However, we believe there are situations where the risk emerges even the infrastructures up and running. Moreover, the lack of modeling framework and the aided-tool make the process even harder. In this paper, we propose a framework to support modeling and analysis of BCP from the organization perspective, where risks and treatments are modeled and analyzed along strategic objectives and their realizations. An automated reasoner based on cost-benefit analysis techniques is proposed to elicit and then adopt the most cost-efficient plan. The approach is developed using the Tropos Goal-Risk Framework and the Time Dependency and Recovery Model as underlain frameworks. A Loan Originating Process case study is used as a running example to illustrate the proposal.

Recent years have seen rapid progress in the development of ontologies as semantic models intended to capture and represent aspects of the real world. There is, however, great variation in the quality of ontologies. If ontologies are to become progressively better in the future, more rigorously developed, and more appropriately compared, then a systematic discipline of ontology evaluation must be created to ensure quality of content and methodology. Systematic methods for ontology evaluation will take into account representation of individual ontologies, performance (in terms of accuracy, domain coverage and the efficiency and quality of automated reasoning using the ontologies) on tasks for which the ontology is designed and used, degree of alignment with other ontologies and their compatibility with automated reasoning. A sound and systematic approach to ontology evaluation is required to transform ontology engineering into a true scientific and engineering discipline. This chapter discusses issues and problems in ontology evaluation, describes some current strategies, and suggests some approaches that might be useful in the future.

When three or more ontologies have been aligned, longer chains of mapped concepts start to appear. In this paper, we empirically study the nature of these composite mappings, focusing on chains of (near) equivalence links of length two. We ask human experts to evaluate samples of composite mappings, taken from large real life data sets. Based on these evaluations, we

Despite their di erent perspectives, arti cial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decision-theoretic framework. Recent analyses of the restrictions of several traditional AI reasoning techniques, coupled with the development of more tractable and expressive decisiontheoretic representation and inference strategies, have stimulated renewed interest in decision theory and decision analysis. We describe early experience with simple probabilistic schemes for automated reasoning, review the dominant expert-system paradigm, and survey some recent research at the crossroads of AI and decision science. In particular, we present the belief network and in uence diagram representations. Finally, we discuss issues that have not been studied in detail within the expert-systems setting, yet are crucial for developing theoretical methods and computational architectures for automated reasoners.

We report the results of a first implementation demonstrating the use of an ontology to support reasoning about obstacles to improve the capabilities and performance of on-board route planning for autonomous vehicles. This is part of an overall effort to evaluate the performance of ontologies in different components of an autonomous vehicle within the 4D/RCS system architecture developed at NIST. Our initial focus has been on simple roadway driving scenarios where the controlled vehicle encounters potential obstacles in its path. As reported elsewhere [C. Schlenoff, S. Balakirsky, M. Uschold, R. Provine, S. Smith, Using ontologies to aid navigation planning in autonomous vehicles, Knowledge Engineering Review 18 (3) (2004) 243-255], our approach is to develop an ontology of objects in the environment, in conjunction with rules for estimating the damage that would be incurred by collisions with different objects in different situations. Automated reasoning is used to estimate collision damage; this information is fed to the route planner to help it decide whether to plan to avoid the object. We describe the results of the first implementation that integrates the ontology, the reasoner and the planner. We describe our insights and lessons learned and discuss resulting changes to our approach.

The Description Logic (DL) research of the last 20 years was mainly concerned with increasing the expressive power of the employed description language without losing the ability of implementing highly-optimized reasoning systems that behave well in practice, inspite of the ever increasing worst-case complexity of the underlying inference problems. OWL DL, the standard ontology language for the Semantic Web, is based on such an expressive DL for which reasoning is highly intractable. Its sublanguage OWL Lite was intended to provide a tractable version of OWL, but turned out to be only of a slightly lower worst-case complexity than OWL DL. This and other reasons have led to the development of two new families of light-weight DLs, EL and DL-Lite, which recently have been proposed as profiles of OWL 2, the new version of the OWL standard. In this paper, we give an introduction to these new logics, explaining the rationales behind their design.

Simulating human language understanding on the computer is a great challenge. A way to approach it is to represent natural language meanings in logic, and to use logical provers to determine what does and does not follow from a text. What logic is best to use and how natural language meanings are best represented in it are far from trivial questions. This thesis focuses on semantic representation in deep parsing. It describes the Delilah parser and generator for Dutch, which computes semantic representations for sentences, discussing several issues and proposing some further improvements to the system. A style of logical form is developed that is optimized for inference in mainly two ways. One is the implementation of event semantics for verbs and nominalizations and with underlying states for intersective adjectives and their corresponding abstract nouns. This makes many entailments follow straightforwardly. The second is the introduction of Flat Logical Form, as an alternative to ...

This paper provides a detailed description of the CNF part of the TPTP Problem Library for automated theorem-proving systems. The library is available via the Internet and forms a common basis for development and experimentation with automated theorem provers. This paper explains the motivations and reasoning behind the development of the TPTP (thus implicitly explaining the design decisions made) and describes the TPTP contents and organization. It also provides guidelines for obtaining and using the library, summary statistics about release v1.2.1, and an overview of the tptp2X utility program. References for all the sources of TPTP problems are provided.

The paper compares two popular strategies for solving propositional satisfiability, backtracking search and resolution, and analyzes the complexity of a directional resolution algorithm (DR) as a function of the "width" (w * ) of the problem's graph. Our empirical evaluation confirms theoretical prediction, showing that on low-w * problems DR is very efficient, greatly outperforming the backtracking-based Davis-Putnam-Logemann-Loveland procedure (DP). We also emphasize the knowledge-compilation properties of DR and extend it to a tree-clustering algorithm that facilitates query answering. Finally, we propose two hybrid algorithms that combine the advantages of both DR and DP. These algorithms use control parameters that bound the complexity of resolution and allow time/space trade-offs that can be adjusted to the problem structure and to the user's computational resources. Empirical studies demonstrate the advantages of such hybrid schemes.

A long-standing goal of AI is to enable robots to plan in the face of uncertain and incomplete information, and to handle task failure intelligently. This paper shows how to achieve this. There are two central ideas. The first idea is to organize the robot's knowledge into three layers: instance knowledge at the bottom, commonsense knowledge above that, and diagnostic knowledge on top. Knowledge in a layer above can be used to modify knowledge in the layer(s) below. The second idea is that the robot should represent not just how its actions change the world, but also what it knows or believes. There are two types of knowledge effects the robot's actions can have: epistemic effects (I believe X because I saw it) and assumptions (I'll assume X to be true). By combining the knowledge layers with the models of knowledge effects, we can simultaneously solve several problems in robotics: (i) task planning and execution under uncertainty; (ii) task planning and execution in open worlds; (iii) explaining task failure; (iv) verifying those explanations. The paper describes how the ideas are implemented in a three-layer architecture on a mobile robot platform. The robot implementation was evaluated in five different experiments on object search, mapping, and room categorization.

In this chapter, we report the design of an ontology (called onto-ServSys) for the relevant service system construct used in the Service Science, Management and Engineering (SSME) arena. These conceptual artifacts have been used to establish a non-ambiguous and common set of basic constructs for supporting automated reasoning on a domain of interest in intelligent systems. While this reported ontology is initial, and still conceptual, we claim it is useful to advance on an integrated view of service systems, which at present, given the multiple and disparate literatures, is fragmented and disperse. Such integration is realized through a Systems Approach, and its design is guided by a conceptual design research method.

In this paper, the authors describe their initial investigations in computational metaphysics. Our method is to implement axiomatic metaphysics in an automated reasoning system. In this paper, we describe what we have discovered when the theory of abstract objects is implemented in prover9 (a first-order automated reasoning system which is the successor to otter). After reviewing the second-order, axiomatic theory of abstract objects, we show (1) how to represent a fragment of that theory in prover9’s first-order syntax, and (2) how prover9 then finds proofs of interesting theorems of metaphysics, such as that every possible world is maximal. We conclude the paper by discussing some issues for further research.

Ontologies are commonly used in computer science either as a reference model to support semantic interoperability, or as an artifact that should be efficiently represented to support tractable automated reasoning. This duality poses a tradeoff between expressivity and computational tractability that should be addressed in different phases of an ontology engineering process. The inadequate choice of a modeling language, disregarding the goal of each ontology engineering phase, can lead to serious problems in the deployment of the resulting model. This article discusses these issues by making use of an industrial case study in the domain of Oil and Gas. We make explicit the differences between two different representations in this domain, and highlight a number of concepts and ideas that were implicit in an original OWL-DL model and that became explicit by applying the methodological directives underlying an ontologically well-founded modeling language. Gas domain representation of a shared conceptualization, i.e., a concrete artifact representing a model of consensus within a community and a universe of discourse. Moreover, in this sense of a reference model, an ontology is primarily aimed at supporting semantic interoperability in its various forms (e.g, model integration, service interoperability, knowledge harmonization, and taxonomy alignment); (ii) secondly, the discussion regarding representation mechanisms for the construction of domain ontologies is, typically, centered on computational issues, not truly ontological ones.

Efficient multimedia event processing is a key enabler for real-time and complex decision making in streaming media. The need for expressive queries to detect high-level human-understandable spatial and temporal events in multimedia streams is inevitable due to the explosive growth of multimedia data in smart cities and internet. The recent work in stream reasoning, event processing, and visual reasoning inspires the integration of visual and commonsense reasoning in multimedia event processing, which would improve and enhance multimedia event processing in terms of ex-pressivity of event rules and queries. This can be achieved through careful integration of knowledge about entities, relations and rules from rich knowledge bases via reasoning over multimedia streams within an event processing engine. The prospects of neuro-symbolic visual reasoning within multimedia event processing are promising, however, there are several associated challenges that are highlighted in this paper.

We introduce a semi-automated proof system for basic category-theoretic reasoning. It is based on a first-order sequent calculus that captures the basic properties of categories, functors and natural transformations as well as a small set of proof tactics that automate proof search in this calculus. We demonstrate our approach by automating the proof that the functor categories Fun[C × D, E] and Fun [C, Fun[D, E]] are naturally isomorphic.

Since the 1980s, two approaches have been developed for analyzing security protocols. One of the approaches relies on a computational model that considers issues of complexity and probability. This approach captures a strong notion of security, guaranteed against all probabilistic polynomial-time attacks. The other approach relies on a symbolic model of protocol executions in which cryptographic primitives are treated as black boxes. Since the seminal work of Dolev and Yao, it has been realized that this latter approach enables significantly simpler and often automated proofs. However, the guarantees that it offers have been quite unclear.

Despite their di erent perspectives, arti cial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decision-theoretic framework. Recent analyses of the restrictions of several traditional AI reasoning techniques, coupled with the development of more tractable and expressive decisiontheoretic representation and inference strategies, have stimulated renewed interest in decision theory and decision analysis. We describe early experience with simple probabilistic schemes for automated reasoning, review the dominant expert-system paradigm, and survey some recent research at the crossroads of AI and decision science. In particular, we present the belief network and in uence diagram representations. Finally, we discuss issues that have not been studied in detail within the expert-systems setting, yet are crucial for developing theoretical methods and computational architectures for automated reasoners.

Graphical User Interface (GUI) based systems are flourishing their popularity in the industrial area in
present days. If it is possible to integrate the GUI based systems with a Global System for Mobile (GSM)
modem for controlling and monitoring, it will be magnificently acceptable communication system. In this
paper a method is presented on a distant machine and motor control system monitoring and controlling,
using a GSM modem and GUI. PIC microcontroller is used to bridge and synchronize the operation of GUI
and GSM systems.

In this paper we present a theorem proving methodology for a restricted but signican t fragment of the conditional language made up of (boolean combinations of) conditional statements with unnested antecedents. The method is based on the possible world semantics for conditional logics. The label formalism introduced in (AG94,ABGR96) to account for the semantics of normal modal logics is easily

1 2 This formulation ensures that linear constraint conditions such as w(θ) = a , w(φ | ψ) = b , and w(ψ | θ) ≤ c , where a, b, c ∈ [0, 1] and θ , φ , and ψ are Boolean combinations of the α j 's, are all permissible in K provided that the resulting constraint set K is consistent. Here a conditional constraint such as w(ψ | θ) ≤ c is interpreted as w(ψ ∧ θ) ≤ c w(θ) which is always a well-defined linear constraint, albeit vacuous when w(θ) = 0 .. See e.g.

The logical foundations of the standard web ontology languages are provided by expressive Description Logics (DLs), such as SHIQ and SHOIQ. In the Semantic Web and other domains, ontologies are increasingly seen also as a mechanism to access and query data repositories. This novel context poses an original combination of challenges that has not been addressed before: (i) sufficient expressive power of the DL to capture common data modelling constructs; (ii) well established and flexible query mechanisms such as those inspired by database technology; (iii) optimisation of inference techniques with respect to data size, which typically dominates the size of ontologies. This calls for investigating data complexity of query answering in expressive DLs. While the complexity of DLs has been studied extensively, few tight characterisations of data complexity were available, and the problem was still open for most DLs of the SH family and for standard query languages like conjunctive queries and their extensions. We tackle this issue and prove a tight CONP upper bound for positive existential queries with no transitive roles in SHOQ, SHIQ, and SHOI. We thus establish that, for a whole range of sublogics of SHOIQ that contain AL, answering such queries has CONP-complete data complexity. We obtain our result by a novel tableaux-based algorithm for checking query entailment, which uses a modified blocking condition in the style of CARIN. The algorithm is sound for SHOIQ, and shown to be complete for all considered proper sublogics in the SH family.

In this work we exploit a recently introduced nonmono-tonic extension of Description Logics, able to deal with the problem of knowledge invention via commonsense concept combination, to dynamically generate novel editorial contents in the context of a real broadcasting company: RAI-Radiotelevisione Italiana, the Ital-ian public broadcaster. In particular, we introduce the system implementing such logic, i.e. DENOTER: Dynamic gEnerator of NOvel contents in mulTimEdia bRoadcasting (available online at the URL: http://di.unito.it/denoter), that has been applied and tested in the online multimedia platform of RAI (i.e. RaiPlay) as a tool for both the generation/suggestion of novel genres of multime-dia on-demand contents and the reclassification of the available items within such new genres. Our system works by extracting the typical properties characterizing the available genres (with a standard information extraction pipeline) and by building novel classes of genres as the result of a creative combination of such extracted representations. We have tested DENOTER (i) by reclassifying the available contents in RaiPlay with respect to the new generated genres (ii) with an evaluation, in the form of a controlled user study experiment, of the feasibility of using the obtained reclassifications as recommended contents (iii) with a qualitative evaluation done with a small group of experts of RAI. The obtained results are encouraging and pave the way to many possible further improvements and research directions.