The paper addresses the problem of executing a plan in a dynamic environment for tasks involving constraints on consumable resources modeled as numeric fluents. In particular, the paper proposes a novel monitoring and adaptation strategy joining reactivity and proactivity in a unified framework. By exploiting the flexibility of a multi modality plan (where each action can be executed in different modalities), reactivity and proactivity are guaranteed by means of a reconfiguration step. The reconfiguration is performed (i) when the plan is no more valid to recovery from the impasse (reactively), or (ii) under the lead of a kernel based strategy to enforce the tolerance to unexpected situations (proactivity). Both mechanisms have been integrated into a continual planning system and experimentally evaluated over three numeric domains, extensions of planning competition domains. Results show that the approach is able to increase the percentage of cases successfully solved while preserving efficiency in most situations.

Robust execution of exploration mission plans has to deal with limited computational power on-board a planetary rover, and with limited rover's autonomy. Typically, these limitations prevent the rover to synthesize a new mission plan when some unexpected contingency arises. The paper shows that when such deviations refers to anomalies on the consumption of resources, robust execution can be achieved efficiently through an action reconfiguration approach instead of a replanning from scratch. Building up on an extended action model representation, the paper proposes an effective continual planner - ReCon - that, exploiting a general purpose CSP solver, is able to (i) detect violations of mission resource constraints, and (ii) find (if any) a new configuration of actions

Diagnosing the execution of a Multiagent Plan (MAP) means identifying and explaining action failures (i.e., actions that did not reach their expected effects). Current approaches to MAP diagnosis are substantially centralized, and assume that action failures are independent of each other. In this paper, the diagnosis of MAPs, executed in a dynamic and partially observable environment, is addressed in a fully distributed and asynchronous way; in addition, action failures are no longer assumed as independent of each other. The paper presents a novel methodology, named Cooperative Weak-Committed Monitoring (CWCM), enabling agents to cooperate while monitoring their own actions. Cooperation helps the agents to cope with very scarcely observable environments: what an agent cannot observe directly can be acquired from other agents. CWCM exploits nondeterministic action models to carry out two main tasks: detecting action failures and building trajectory-sets (i.e., structures representing the knowledge an agent has about the environment in the recent past). Relying on trajectory-sets, each agent is able to explain its own action failures in terms of exogenous events that have occurred during the execution of the actions themselves. To cope with dependent failures, CWCM is coupled with a diagnostic engine that distinguishes between primary and secondary action failures. An experimental analysis demonstrates that the CWCM methodology, together with the proposed diagnostic inferences, are effective in identifying and explaining action failures even in scenarios where the system observability is significantly reduced.

The paper addresses the task of monitoring and diagnosing the execution of a multi-agent plan (MAP) which involves actions concurrently executed by a team of cooperating agents. The paper describes a weak commitment strategy to deal with cases where observability is only partial and it is not sufficient for inferring the outcome of all the actions executed so far. The paper discusses the role of target actions in providing sufficient conditions for inferring the pending outcomes in a finite time window. The action outcome provides the basis for computing plan diagnosis and for singling out the goals which will not be achieved because of an action failure.

The paper addresses the problem of finding a compact representation of the diagnoses within a model-based approach to diagnosis. To this end, we introduce the notion of scenario, a special kind of CNF formula over the component variables, which can be used to encode a large number of diagnoses using the same amount of space needed for encoding just a single diagnosis. We show how the solutions to a diagnostic problem can be computed as sets of scenarios by presenting first an exhaustive algorithm and then an efficient algorithm, which exploits probabilistic information to restrict the result set to preferred scenarios. Finally, we discuss the issue of how to efficiently extract preferred diagnoses from sets of scenarios and characterize a class of system models for which our techniques perform particularly well. Concepts and algorithms introduced in the paper have been tested within the prototype of the diagnostic agent of a space robotic arm; resulting statistics are reported and critically discussed.

The definition of suitable case‐base maintenance policies is widely recognized as a major key to success for case‐based systems; underestimating this issue may lead to systems that either do not fulfill their role of knowledge management and preservation or that do not perform adequately under performance dimensions, namely, computation time and competence and quality of solutions. The goal of this article is to analyze some automatic case‐base management strategies in the context of a multimodal architecture combining case‐based reasoning and model‐based reasoning. We propose and compare two different methodologies, the first one, called replace, is a competence‐based strategy aimed at replacing a set of stored cases with the current one, if the latter exhibits an estimated competence comparable with the estimated competence of the considered set of stored cases. The second one, called learning by failure with forgetting (LFF), is based on incremental learning of cases interleaved with off‐line processes of forgetting (deleting) cases whose usage does not fulfill specific utility conditions. Results from an extensive experimental analysis in an industrial plant diagnosis domain are reported, showing the usefulness of both strategies with respect to the maintenance of suitable performance levels for the target system.

In this paper we focus on the temporal constraints between causes and effects of causal relations, and, to deal correctly with such relations, we stress the importance of analyzing the action‐types (aspectual category) of causes and effects. In particular, we propose a domain‐independent ontology in which the distinctions between action‐types (e.g., the distinction between durative and punctual situations) are dealt with, and different types of causal relations are distinguished, on the basis of (i) the temporal constraints they impose between causes and effects (these constraints are expressed in a temporal formalism that extends Vilain's point interval algebra) and (ii) the action‐types of their causes and effects. Our ontology allows one to capture precisely the temporal constraints imposed by causation and the action‐types of the related situations. Moreover, in case the user has no accurate knowledge about the action‐types of some situations and/or the types of some causal connections to be dealt with, our formalism allows the user to leave the descriptions underspecified, and more specific pieces of information may be inferred. Inferences provide a flow of information about action‐types to information about temporal constraints in causation and vice versa, and demonstrate that a deep integration of time and causation is provided also at the inferential level.

The article focuses on the relations between time and causation and proposes a general framework in which a strict integration is achieved both in the representation formalism and in the reasoning process. By taking advantage of the basic capabilities of a Hybrid Knowledge Representation formalism (in particular, we use BACK), we provide an explicit and integrated representation of the basic entities in the temporal and causal ontology, and propose a taxonomy in which different types of causal relations found in the literature are classified, depending on the temporal relations they impose between causes and effects. Moreover, we develop a specialized causal-temporal reasoner which operates on causal nets built upon the basic causal-temporal representation. Such a reasoner is flexible (in the sense that different definitions of causation may be considered in the same reasoning process) and integrated (since the mutual effects of time and causation are coped with), and takes advantage of a specialized temporal reasoner. © 1994 John Wiley & Sons, Inc.

An approach to the synthesis and use of operational knowledge in diagnostic problem solving is proposed. The approach significantly departs from previous approaches to knowledge compilation in the sense that the authors do not aim at compiling an autonomous heuristic problem solver from a deep one but only at deriving a set of conditions whose evaluation can focus and speed up diagnostic reasoning on casual models. In particular, it is argued that operational necessary conditions can be used to focus causal diagnostic reasoning by pruning significant portions of the search space to be considered. The process for synthesizing operational knowledge is performed by running a case-independent simulation on a casual model using constraint propagation techniques. This is a major difference with respect to approaches based on the use of examples. Many of the problems arising in the other approaches to the synthesis of heuristics from deep knowledge are solved in this system.>

In this paper, we analyze the logical definitions of model‐based diagnosis recently presented in the literature, and we propose a unified framework (based on the integration of abductive and consistency‐based reasoning) in which most of such definitions can be captured. This allows us to single out the existence of a spectrum of alternatives in the logical definition of diagnosis. A lot of attention in the paper is devoted to analyzing the differences among the definitions in the spectrum. In particular, we show that the definitions can be compared on the basis of their restrictive‐ness and we relate such a restrictiveness with the completeness of the model of the system to be diagnosed. Dans cet article, les auteurs analysent les définitions logiques de diagnostics basés sur un modèle dont il a été question récemment dans certains ouvrages. Ils proposent un cadre unifié (basé sur l'intégration du raisonnement abductif et du raisonnement basé sur la consistance) à l'interieur duquel la plupart de ces définitions peuvent ětre regroupées. Cette particularité permet de mettre en lumière l'existence d'un spectre d'alternatives dans la définition logique du diagnostic. Cet article accorde une attention toute particulière à l'analyse des différences entre les définitions du spectre. En outre, les auteurs démontrent que les définitions peuvent ětre compareées en fonction de leur caractère restrictif, qui est ensuite mis en relation avec la complétude du modèle du système faisant l'objet d'un diagnostic.

The aim of this paper is to analyse from various points of view the relationships between abduction and deduction. In particular, we consider a meta-level definition of abduction in terms of deduction, similar to various definitions proposed in the literature, and an object-level definition in which abductive conclusions are expressed as a logicalconsequence of the observations and of a simple transformation of the domain theory based on predicate completion. The equivalence between the two definitions is proved for domain theories of considerable expressive power. The object-level characterization we propose uses very simple forms of reasoning and the equivalence result allows us to make explicit some of the assumptions underlying meta-level definitions of abduction. The use of predicate completion in characterizing abductive explanations shows a relation between abduction and foundations of logic programming.

The aim of the present paper is to show how a rule-based parser for the Italian language has been extended to analyze sentences involving conjunctions. The most noticeable fact is the ease with which the required modifications fit in the previous parser structure. In particular, the rules written for analyzing simple sentences (without conjunctions) needed only small changes. On the contrary, more substantial changes were made to the exception-handling rules (called "natural changes") that are used to restructure the tree in case of failure of a syntactic hypothesis. The parser described in the present work constitutes the syntactic component of the FIDO system (a Flexible Interface for Database Operations), an interface allowing an end-user to access a relational database in natural language (Italian).

The paper discusses three different kinds of syntactic ill-formedness: ellipsis, conjunctions, and actual syntactic errors. It is shown how a new grammatical formalism, based on a two-level representation of the syntactic knowledge is used to cope with ill-formed sentences. The basic control structure of the parser is briefly sketched; the paper shows that it can be applied without any substantial change both to correct and to ill-formed sentences. This is achieved by introducing a mechanism for the hypothesization of syntactic structures, which is largely independent of the rules defining the well-formedness. On the contrary, the second level of syntactic knowledge embodies those rules and is used to validate the hypotheses emitted by the first level. Alternative hypotheses are obtained, when needed, by means of local reorganizations of the parse tree. Sentence fragments are handled by the same mechanism, but in this case the second level rules are used to detect the absence of one (or more) constituents.

A recognizer of meaningful sequences of isolated words spoken in the Italian language is presented. The system has been designed in a modular fashion and the current version has been obtained by adding the syntactic and semantic levels to the isolated-word recognizer previously developed by the same authors. The lexical, syntactic and semantic levels are integrated into a hierarchical knowledge source represented by a transition network grammar. The parsing is directed by the control unit and is performed by means of a combined strategy of bottom-up and top-down techniques, using a modified version of the Earley algorithm. Lexical hypotheses are verified at the phonemic level assigning them a score based on a distance measure evaluated by an error-correcting parsing algorithm. The protocol used in the preliminary experi - ments is a robot command language.

The paper reports a detailed analysis of the performances ofan Italian-word recognition system structured into a hierarchy of processes controlled by rules of appropriate grammars. The knowledge sources of each recognition level can be learned by experiments and adapted to new vocabularies and new speakers. A set of experiments has been carried out in order to evaluate the influence of vocabulary size and speaker variation over the performances of each recognition step. The aim is to identify a set of parameters whose values can be extracted from a small set of training words spoken by a new speaker and which can be used to automatically adapt the system to the speaker.

The rules for the generation of the pitch contour of a fragment of the Italian language are presented. The generative model, of which the input is a formal description of the sentence and outputs the pitch contour, operates as an automaton. The basic elements for the pitch curve generation are the segments between two accents. Inside these segments the pitch curves is approximated by means of simple primitives. The pitch rules are used to control a digital speech synthesiser.

Some methodologles based on fuzzy set theory are proposed for emitting and verifying lexical hypotheses in a Speech Understanding System. Context-independent and context-dependent features are extracted from spectrogram patterns with a procedure that establishes a correspondence relation between a speech segment and its linguistic interpretation. The syllabic hypotheses are used for emitting lexical hypotheses by an algorithm that compensates for partial corruption of the syllabic information exploiting the lexical redundancy.