Goal-directed aimed movements with path obstructions

Studies are reported in which participants moved from a starting position to a final target, but moved around obstacles that prevented a direct path being taken. Seven experiments are reported in which subjects made multiple-component movements, defined by changes in the direction of movement, but without intervening targets (or stopping points) between components. It was found that components of movement time did not interact, contrary to the results of Gan and Hoffmann (1988b) for cases where there was an intervening target, and that component times could be added. For ballistic components, the time for the movement was linearly related to the square root of the total amplitude of movement; for components that were visually controlled, the time was related to Fitts' Index of Difficulty based on the total amplitude of movement and the final target size. Results are compared to data for movements with intervening targets, where the movement integration hypothesis is valid. PRACTITIONER SUMMARY: Many movements, particularly to inaccessible places, cannot be made directly, but must be made about obstacles in the path. This research shows how times for these movements can be quantified in terms of the direct path length and the size and location of the obstacles.     

Goal-directed evaluation of binarization methods

This paper presents a methodology for evaluation of low-level image analysis methods, using binarization (two-level thresholding) as an example. Binarization of scanned gray scale images is the first step in most document image analysis systems. Selection of an appropriate binarization method for an input image domain is a difficult problem. Typically, a human expert evaluates the binarized images according to his/her visual criteria. However, to conduct an objective evaluation, one needs to investigate how well the subsequent image analysis steps will perform on the binarized image. We call this approach goal-directed evaluation, and it can be used to evaluate other low-level image processing methods as well. Our evaluation of binarization methods is in the context of digit recognition, so we define the performance of the character recognition module as the objective measure. Eleven different locally adaptive binarization methods were evaluated, and Niblack's method gave the best performance     

以目标为导向的时序图规划算法的研究

本文提出了一种从目标集扩张时序规划图的新算法。此算法与现有算法不同。采用逆向扩张、正向搜索的策略：由于扩张保留了关于目标的信息。实现了动作方案的完全量化。在搜索阶段。只搜索相关可用动作的交叉点,减少了搜索代价,极大地提高了搜索效率。     

Goal-directed critiquing by computer: ventilator management

VQ-ATTENDING is an expert system which critiques aspects of the ventilator management of a patient receiving mechanical respiratory support. To use VQ-ATTENDING, the physician first inputs medical information describing a patient, a current set of arterial blood gas results, the current ventilator settings, and a proposed set of new ventilator settings. VQ-ATTENDING then critiques the appropriateness of the proposed settings. In its current developmental implementation, VQ-ATTENDING explores a particular expert system designed feature: the ability to assess appropriate treatment goals, and to use those goals to guide the system's critiquing analysis.     

Goal-directed classification using linear machine decision trees

Recent work in feature-based classification has focused on nonparametric techniques that can classify instances even when the underlying feature distributions are unknown. The inference algorithms for training these techniques, however, are designed to maximize the accuracy of the classifier, with all errors weighted equally. In many applications, certain errors are far more costly than others, and the need arises for nonparametric classification techniques that can be trained to optimize task-specific cost functions. This correspondence reviews the linear machine decision tree (LMDT) algorithm for inducing multivariate decision trees, and shows how LMDT can be altered to induce decision trees that minimize arbitrary misclassification cost functions (MCF's). Demonstrations of pixel classification in outdoor scenes show how MCF's can optimize the performance of embedded classifiers within the context of larger image understanding systems     

Qualitative reasoning with directional relations

Qualitative spatial reasoning (QSR) pursues a symbolic approach to reasoning about a spatial domain. Qualitative calculi are defined to capture domain properties in relation operations, granting a relation algebraic approach to reasoning. QSR has two primary goals: providing a symbolic model for human common-sense level of reasoning and providing efficient means for reasoning. In this paper, we dismantle the hope for efficient reasoning about directional information in infinite spatial domains by showing that it is inherently hard to decide consistency of a set of constraints that represents positions in the plane by specifying directions from reference objects. We assume that these reference objects are not fixed but only constrained through directional relations themselves. Known QSR reasoning methods fail to handle this information.     

Taxonomic reasoning with many-sorted logics

This paper provides an introduction to many-sorted logics and motivates their use for representation and reasoning. Perhaps the most important reason to be interested in many-sorted logic is that computational efficiency can be achieved because the search space can be smaller and the length of a derivation shorter than in unsorted logic. There are many possible many-sorted logics of varying degrees of expressiveness, and the dimensions in which many-sorted logics differ are outlined and logics at various points in this space described. The relationship of many-sorted logic to unsorted logic is discussed and the reason why many-sorted logics derivations may be shorter is demonstrated. The paper concludes with a discussion of some many-sorted logic programming languages and some implementation issues.     

Legal reasoning with subjective logic

Judges and jurors must make decisions in an environment of ignoranceand uncertainty for example by hearing statements of possibly unreliable ordishonest witnesses, assessing possibly doubtful or irrelevantevidence, and enduring attempts by the opponents to manipulate thejudge's and the jurors' perceptions and feelings. Three importantaspects of decision making in this environment are the quantificationof sufficient proof, the weighing of pieces of evidence, and therelevancy of evidence. This paper proposes a mathematical frameworkfor dealing with the two first aspects, namely the quantification ofproof and weighing of evidence. Our approach is based on subjectivelogic, which is an extension of standard logic and probability theory,in which the notion of probability is extended by including degrees ofuncertainty. Subjective Logic is a framework for modelling humanreasoning and we show how it can be applied to legalreasoning.     

Approximate reasoning with linguistic modifiers

We analyze the influence of some usual linguistic modifiers, such as scalar product, normalization, Bouchon-Meunier modifiers, perturbation, and (weakening and reinforcement) power, in the process of approximate reasoning and clarify the difference between the conclusions of fuzzy modus ponens in which linguistic modifiers appear and do not appear in premises. © 1998 John Wiley & Sons, Inc.     

Safe reasoning with Logic LTS

Previous work has introduced the setting of Logic Labelled Transition Systems, called Logic LTS or LLTS for short, together with a variant of ready simulation as its fully-abstract refinement preorder, which allows one to compose operational specifications using a CSP-style parallel operator and the propositional connectives conjunction and disjunction.In this article, we show how a temporal logic for specifying safety properties may be embedded into LLTS so that (a) the temporal operators are compositional for ready simulation; (b) ready simulation, when restricted to pairs of processes and formulas, coincides with the logic’s satisfaction relation; (c) ready simulation, when restricted to formulas, is entailment.The utility of this setting as a semantic foundation for mixed operational and temporal-logic specification languages is demonstrated by means of a simple example. We also adopt the concept of may- and must-transitions from modal transition systems for notational convenience, and investigate the relation between modal refinement on modal transition systems and ready simulation on LLTS.     

Qualitative reasoning with imprecise probabilities

This paper investigates the possibility of performing automated reasoning in probabilistic knowledge bases when probabilities are expressed by means of linguistic quantifiers. Data are expressed in terms of ill-known conditional probabilities represented by linguistic terms. Each linguistic term is expressed as a prescribed interval of proportions. Then instead of propagating numbers, qualitative terms are propagated in accordance with the numerical interpretation of these terms. The quantified syllogism, modeling the chaining of probabilistic rules, is studied in this context. It is shown that a qualitative counterpart of this syllogism makes sense and is fairly independent of the thresholds defining the linguistically meaningful intervals, provided that these threshold values remain in accordance with the intuition. The inference power is less than a full-fledged probabilistic constraint propagation device but corresponds better to what could be thought of as commonsense probabilistic reasoning. Suggestions that may improve the inferencing power in the qualitative setting are proposed.This paper is an extended and revised version of a paper entitled A Symbolic Approach to Reasoning with Linguistic Quantifiers inProc. 8th Conf. Uncertainty in Artificial Intelligence Dubois, D., Wellman, M.P., D'Ambrosio, B., and Smets, Ph. (Eds.), Morgan Kaufmann, pp. 74–82, 1992.     

Constraint reasoning with learning automata

This article presents a decision-maker model, called learning automaton, exhibiting adaptive behavior in highly uncertain stochastic environments. This learning model is used in solving constraint satisfaction problems (CSPs) by a procedure that can be viewed as hill climbing in probability space. the use of a fast learning algorithm that relaxes previous common assumptions is investigated. It is proven that the algorithm converges with probability 1 to a solution of the CSP and a set of test problems show that good performance can be achieved. In particular, it is shown that this method achieves a higher level of performance than that presented in a previous similar approach. Finally, it is estimated the speedup of a parallel implementation and the proposed algorithm is compared with a backtracking algorithm enhanced with standard CSP techniques. © 1994 John Wiley & Sons, Inc.     

Approximate reasoning with credible subsets

The complexity of logical inference in knowledge systems grows with the system size, determined, in particular, by the number of its clauses. This growth is so rapid, that reasoning in sizeable systems becomes intractable.However, given a system S and a query Q, should we be able to detect a subset of S informative enough to provide a correct answer to Q, and small enough to fit into the range of tractable computation,the answer to Q could be produced efficiently. This paper presents a way of implementing this idea.     

A logic for reasoning with inconsistency

Most known computational approaches to reasoning have problems when facing inconsistency, so they assume that a given logical system is consistent. Unfortunately, the latter is difficult to verify and very often is not true. It may happen that addition of data to a large system makes it inconsistent, and hence destroys the vast amount of meaningful information. We present a logic, called APC (annotated predicate calculus; cf. annotated logic programs of [4, 5]), that treats any set of clauses, either consistent or not, in a uniform way. In this logic, consequences of a contradiction are not nearly as damaging as in the standard predicate calculus, and meaningful information can still be extracted from an inconsistent set of formulae. APC has a resolution-based sound and complete proof procedure. We also introduce a novel notion of epistemic entailment and show its importance for investigating inconsistency in predicate calculus as well as its application to nonmonotonic reasoning. Most importantly, our claim that a logical theory is an adequate model of human perception of inconsistency, is actually backed by rigorous arguments.A preliminary report on this research appeared in LICS'89.Work of M. Kifer was supported in part by the NSF grants DCR-8603676, IRI-8903507.Work of E. L. Lozinskii was supported in part by Israel National Council for Research and Development under the grants 2454-3-87, 2545-2-87, 2545-3-89 and by Israel Academy of Science, grant 224-88.     

Nonmonotonic reasoning with multiple belief sets

In complex reasoning tasks it is often the case that there is no single, correct set of conclusions given some initial information. Instead, there may be several such conclusion sets, which we will call belief sets. In the present paper we introduce nonmonotonic belief set operators and selection operators to formalize and to analyze structural aspects of reasoning with multiple belief sets. We define and investigate formal properties of belief set operators as absorption, congruence, supradeductivity and weak belief monotony. Furthermore, it is shown that for each belief set operator satisfying strong belief cumulativity there exists a largest monotonic logic underlying it, thus generalizing a result for nonmonotonic inference operations. Finally, we study abstract properties of selection operators connected to belief set operators, which are used to choose some of the possible belief sets. This revised version was published online in June 2006 with corrections to the Cover Date.     

From case-based reasoning to traces-based reasoning

CBR is an original AI paradigm based on the adaptation of solutions of past problems in order to solve new similar problems. Hence, a case is a problem with its solution and cases are stored in a case library. The reasoning process follows a cycle that facilitates “learning” from new solved cases. This approach can be also viewed as a lazy learning method when applied for task classification. CBR is applied for various tasks as design, planning, diagnosis, information retrieval, etc. The paper is the occasion to go a step further in reusing past unstructured experience, by considering traces of computer use as experience knowledge containers for situation based problem solving.     

Spatial reasoning with rectangular cardinal relations

Qualitative spatial representation and reasoning plays a important role in various spatial applications. In this paper we introduce a new formalism, we name RCD calculus, for qualitative spatial reasoning with cardinal direction relations between regions of the plane approximated by rectangles. We believe this calculus leads to an attractive balance between efficiency, simplicity and expressive power, which makes it adequate for spatial applications. We define a constraint algebra and we identify a convex tractable subalgebra allowing efficient reasoning with definite and imprecise knowledge about spatial configurations specified by qualitative constraint networks. For such tractable fragment, we propose several polynomial algorithms based on constraint satisfaction to solve the consistency and minimality problems. Some of them rely on a translation of qualitative networks of the RCD calculus to qualitative networks of the Interval or Rectangle Algebra, and back. We show that the consistency problem for convex networks can also be solved inside the RCD calculus, by applying a suitable adaptation of the path-consistency algorithm. However, path consistency can not be applied to obtain the minimal network, contrary to what happens in the convex fragment of the Rectangle Algebra. Finally, we partially analyze the complexity of the consistency problem when adding non-convex relations, showing that it becomes NP-complete in the cases considered. This analysis may contribute to find a maximal tractable subclass of the RCD calculus and of the Rectangle Algebra, which remains an open problem.     

Cooking personas: Goal-directed design requirements in the kitchen

Technology to assist people in the kitchen has become a major research topic as technology becomes more ubiquitous in the home. Research is being carried out in numerous areas to assist with cooking, from solving difficulties in the kitchen to more recently enhancing already good experiences related to cooking. To design solutions that are not only usable, but useful in all related aspects of cooking, we must get a good understanding of the needs of the user. It is important that real needs are identified, so that products or systems designed are adopted and are sustainable. There are various methods that researchers and designers use to gain user insights and there is much debate on different approaches and their effectiveness. Whilst there have been a number of ethnographic style studies in people's homes, there has been little in the way of understanding user goals in the kitchen which could lead to more effective design solutions. In this paper, we present goal-directed research of cooking needs in Singapore. We produce three primary design personas and three secondary personas, describing their goals and needs and where they can be helped through technology. An online survey was carried out to validate our personas, comparing our qualitative and quantitative findings. We provide a general holistic overview of kitchen requirements for these personas, whether in helping correct problems or enhancing positive experiences and how these needs can be connected. The user requirements provided can help guide researchers and help designers produce more meaningful, complete and acceptable technology solutions in the kitchen.     

Uncertain reasoning about agents' beliefs and reasoning

Reasoning about mental states and processes is important in varioussubareas of the legal domain. A trial lawyer might need to reason andthe beliefs, reasoning and other mental states and processes of membersof a jury; a police officer might need to reason about the conjecturedbeliefs and reasoning of perpetrators; a judge may need to consider adefendant's mental states and processes for the purposes of sentencing;and so on. Further, the mental states in question may themselves beabout the mental states and processes of other people. Therefore, if AIsystems are to assist with reasoning tasks in law, they may need to beable to reason about mental states and processes. Such reasoning isriddled with uncertainty, and this is true in particular in the legaldomain. The article discusses how various different types ofuncertainty arise, and shows how they greatly complicate the task ofreasoning about mental states and processes. The article concentrates onthe special case of states of belief and processes of reasoning, andsketches an implemented, prototype computer program (ATT-Meta) thatcopes with the various types of uncertainty in reasoning about beliefsand reasoning. In particular, the article outlines the system'sfacilities for handling conflict between different lines of argument,especially when these lie within the reasoning of different people. Thesystem's approach is illustrated by application to a real-life muggingexample.