Form,interpretation, and the uniqueness of content: Response to Morris

In response to Michael Morris, I attempt to refute the crucial second premise of the argument, which states that the formality condition cannot be satisfied non-stipulatively in computational systems. I defend the view of representation urged in Meaning and Mental Representation against the charge that it makes content stipulative and therefore irrelevant to the explanation of cognition. Some other reservations are expressed.     

Toward a computational interpretation of situation semantics1

Situation semantics proposes novel and attractive treatments for several problem areas of natural language semantics, such as efficiency (context sensitivity) and prepositional attitude reports. Its focus on the information carried by utterances makes the approach very promising for accounting for pragmatic phenomena. However, situation semantics seems to oppose several basic assumptions underlying current approaches to natural language processing and the design of intelligent systems in general. It claims that efficiency undermines the standard notions of logical form, entailment, and proof theory, and objects to the view that mental processes necessarily involve internal representations. The paper attempts to clarify these issues and discusses the impact of situation semantics’ criticisms for natural language processing, knowledge representation, and reasoning. I claim that the representational approach is the only currently practical one for the design of large intelligent systems, but argue that the representations used should be efficient in order to account for the system's embedding in its environment. The paper concludes by stating some constraints that a computational interpretation of situation semantics should obey and discussing remaining problems.     

Analog and Digital Representation

In this paper, I argue for three claims. The first is that the difference between analog and digital representation lies in the format and not the medium of representation. The second is that whether a given system is analog or digital will sometimes depend on facts about the user of that system. The third is that the first two claims are implicit in Haugeland's (1998) account of the distinction.

Dynamic interpretation and hoare deduction

In this paper we present a dynamic assignment language which extends the dynamic predicate logic of Groenendijk and Stokhof [1991: 39–100] with assignment and with generalized quantifiers. The use of this dynamic assignment language for natural language analysis, along the lines of o.c. and [Barwise, 1987: 1–29], is demonstrated by examples. We show that our representation language permits us to treat a wide variety of donkey sentences: conditionals with a donkey pronoun in their consequent and quantified sentences with donkey pronouns anywhere in the scope of the quantifier. It is also demonstrated that our account does not suffer from the so-called proportion problem.Discussions about the correctness or incorrectness of proposals for dynamic interpretation of language have been hampered in the past by the difficulty of seeing through the ramifications of the dynamic semantic clauses (phrased in terms of input-output behaviour) in non-trivial cases. To remedy this, we supplement the dynamic semantics of our representation language with an axiom system in the style of Hoare. While the representation languages of barwise and Groenendijk and Stokhof were not axiomatized, the rules we propose form a deduction system for the dynamic assignment language which is proved correct and complete with respect to the semantics.Finally, we define the static meaning of a program of the dynamic assignment language as the weakest condition such that terminates successfully on all states satisfying , and we show that our calculus gives a straightforward method for finding static meanings of the programs of the representation language.     

Formalizing multiple interpretation of legal knowledge

A representation methodology for knowledge allowing multiple interpretations is described. It is based on the following conception of legal knowledge and its open texture. Since indeterminate, legal knowledge must be adapted to fit the circumstances of the cases to which it is applied. Whether a certain adaptation is lawful or not is measured by metaknowledge. But as this too is indeterminate, its adaptation to the case must be measured by metametaknowledge, etc. This hierarchical model of law is quite well-established and may serve well as a basis for a legal knowledge system. To account for the indeterminacy of law such a system should support the construction of different arguments for and against various interpretations of legal sources. However, automatizing this reasoning fully is unsound since it would imply a restriction to arguments defending interpretations anticipated at programming time. Therefore, the system must be interactive and the user's knowledge be furnished in a principled way. Contrary to the widespread opinion that classical logic is inadequate for representing open-textured knowledge, the framework outlined herein is given a formalization in first order logic.     

Knowledge organization and its role in representation and interpretation for time-varying data: the ALVEN system

The so-called "first generation" expert systems were rule-based and offered a successful framework for building applications systems for certain kinds of tasks. Spatial, temporal, and causal reasoning, knowledge abstractions, and structuring are among topics of research for "second generation" expert systems. It is proposed that one of the keys for such research is knowledge organization. Knowledge organization determines control structure design, explanation and evaluation capabilities for the resultant knowledge base, and has strong influence on system performance. We are exploring a framework for expert system design that focuses on knowledge organization, for a specific class of input data, namely, continuous, time-varying data (image sequences or other signal forms). Such data are rich in temporal relationships as well as temporal changes of spatial relations, and are thus a very appropriate testbed for studies involving spatio-temporal reasoning. In particular, the representation formalism specifies the semantics of the organization of knowledge classes along the relationships of generalization/specialization, decomposition/aggregation, temporal precedence, instantiation, and expectation-activated similarity. Á hypothesize-and-test control structure is driven by the class organizational principles, and includes several interacting dimensions of search (data-driven, model-driven, goal-driven temporal, and failure-driven search). The hypothesis ranking scheme is based on temporal cooperative computation, with hypothesis "fields of influence" being defined by the hypothesis' organizational relationships. This control structure has proven to be robust enough to handle a variety of interpretation tasks for continuous temporal data. A particular incarnation, the ALVEN system, for left ventricular performance assessment from X-ray image sequences, will be summarized in this paper.     

An interactive computer-based interface to support the discovery of individuals’ mental representations and preferences in decisions problems: An application to travel behavior

Growing emphasis is currently given in decision modeling on process data to capture behavioral mechanisms that ground decision-making processes. Nevertheless, advanced applications to elicit such data are still lacking. The Causal Network Elicitation Technique interview and card-game, both face-to-face interviews, are examples of a behavioral process method to obtain individuals’ decision-making by eliciting temporary mental representations of particular problems. However, to portray and model these representations into formal modeling approaches, such as Bayesian decision networks, an extensive set of parameters has to be gathered for each individual. Thus, data collection procedures for large sample groups can be costly and time consuming. This paper reports on the methodological conversion and enhancement of the existing elicitation methods into a computer-based interface that allows to not only uncover individuals’ mental representations but also to automate the generation of preference parameter elicitation questions. Results of such studies can be used to understand individuals’ constructs and beliefs with respect to decision alternatives, predict individuals’ decision behavior at a disaggregate level, and to assess behavioral changes due to differences in contexts and constraints.     

Security protocols: from linear to classical logic by abstract interpretation

We relate two models of security protocols, namely the linear logic or multiset rewriting model, and the classical logic, Horn clause representation of protocols. More specifically, we show that the latter model is an abstraction of the former, in which the number of repetitions of each fact is forgotten. This result formally characterizes the approximations made by the classical logic model.     

Beyond first aid: emergency response teams turn to graphics

Whether it is a chemical or biological hazard, a fire, a crime in progress, or a life-threatening health situation, the faster the first responders arrive on the scene, the better. In a large urban area, finding the right building or house can prove difficult. The addition of helicopter units to many police departments brought emergency response teams closer to their targets sooner, but with limited success because identifying exact addresses from the air was not inherent in the process. In most cities, the helicopters are responsible for getting to the general area of an emergency situation, where ground crews then direct them. Now a company called MetaMAP has introduced MetaVUE geographic information system (GIS) software that makes identifying exact addresses from the air possible. The paper discusses its application     

Cognitive networks as a guide to menu organization: An application in the automated cockpit

Abstract

When a menu-driven interface controls information in the domain of expertise of the end users, the difficulty that inexperienced users typically have in accessing the panels they seek can be reduced by making the menu organization consistent with the user's cognitive organization. A general tool for deriving a cognitive network is described and applied to the concepts displayed and controlled on the panels of an interface to a flight management system. The cognitive network was used to select the options that would appear on each panel of three different prototypes. The prototypes differed with respect to the amount of redundancy (extra pathways) between a set of dominating panels that appeared on a primary index page and the remaining panels. A fourth prototype was based on the recommendations of a design team rather than the cognitive network derived from a sample of pilots. Four pilots were assigned to each of the four prototypes, in a training session that required the pilots to find specific panels in order to answer questions pertaining to various scenarios. The group using the high redundancy organization that was based on the cognitive network took less time per trial than the three other groups.     

Meaning and Use of Indefinite Expressions

Sentences containing pronouns and indefinite noun phrases can be said toexpress open propositions, propositions which display gaps to be filled.This paper addresses the question what is the linguistic content ofthese expressions, what information they can be said to provide to ahearer, and in what sense the information of a speaker can be said tosupport their utterance. We present and motivate first order notions ofcontent, update and support. The three notions are each defined in acompositional fashion and brought together within a single and coherentframework.     

On Building Robot Persons: Response to Zlatev

Zlatev offers surprisingly weak reasoning in support of his view that robots with the right kind of developmental histories can have meaning. We ought nonetheless to praise Zlatev for an impressionistic account of how attending to the psychology of human development can help us build robots that appear to have intentionality.     

交叉树:一种用于安全协议分析的数据结构

提出用一种特殊的数据结构交叉树来描述安全协议中的消息。交叉树有一些交叉结点，拥有交叉结点的几棵交叉树形成交叉森林。一棵交叉树唯一对应于一个消息，一个交叉森林表示那些在协议执行过程中采用相同机制发送或接收的消息。一个或几个消息中相同的原子消息在交叉树或交叉森林中用交叉结点来表示，这样易于保证原子消息的一致性，以及公钥和它所有者之间的一致性。另外，交叉树还可用于为消息模板建立可接受消息，这时于建立在模型检验基础上的安全协议分析是非常必要的。     

Acoustic signal interpretation: Reasoning with non-specific and uncertain information

An expert system approach to identifying the sources of underwater acoustic signals is described. Much of the information about ocean vessels, which is derived from acoustic features, is non-specific (i.e. pointing only to general classes of vessels). As well, there is often uncertainty both in the accuracy of the feature identification and in the applicability of the rules in the knowledge base. In order to deal with non-specific and uncertain evidence in the presence of an unknown number of signal sources, we develop an inference network approach which is based on the Dempster-Shafer theory of evidence.     

Image interpretation with a conceptual graph: Labeling over-segmented images and detection of unexpected objects

The labeling of the regions of a segmented image according to a semantic representation (ontology) is usually associated with the notion of understanding. The high combinatorial aspect of this problem can be reduced with local checking of constraints between the elements of the ontology. In the classical definition of Finite Domain Constraint Satisfaction Problem, it is assumed that the matching problem between regions and labels is bijective. Unfortunately, in image interpretation the matching problem is often non-univocal. Indeed, images are often over-segmented: one object is made up of several regions. This non-univocal matching between data and a conceptual graph was not possible until a decisive step was accomplished by the introduction of arc consistency with bilevel constraint (FDCSPBC). However, this extension is only adequate for a matching corresponding to surjective functions. In medical image analysis, the case of non-functional relations is often encountered, for example, when an unexpected object like a tumor appears. In this case, the data cannot be mapped to the conceptual graph, with a classical approach. In this paper we propose an extension of the FDCSPBC to solve the constraint satisfaction problem for non-functional relations.     

Beyond Tracking: Modelling Activity and Understanding Behaviour

In this work, we present a unified bottom-up and top-down automatic model selection based approach for modelling complex activities of multiple objects in cluttered scenes. An activity of multiple objects is represented based on discrete scene events and their behaviours are modelled by reasoning about the temporal and causal correlations among different events. This is significantly different from the majority of the existing techniques that are centred on object tracking followed by trajectory matching. In our approach, object-independent events are detected and classified by unsupervised clustering using Expectation-Maximisation (EM) and classified using automatic model selection based on Schwarz's Bayesian Information Criterion (BIC). Dynamic Probabilistic Networks (DPNs) are formulated for modelling the temporal and causal correlations among discrete events for robust and holistic scene-level behaviour interpretation. In particular, we developed a Dynamically Multi-Linked Hidden Markov Model (DML-HMM) based on the discovery of salient dynamic interlinks among multiple temporal processes corresponding to multiple event classes. A DML-HMM is built using BIC based factorisation resulting in its topology being intrinsically determined by the underlying causality and temporal order among events. Extensive experiments are conducted on modelling activities captured in different indoor and outdoor scenes. Our experimental results demonstrate that the performance of a DML-HMM on modelling group activities in a noisy and cluttered scene is superior compared to those of other comparable dynamic probabilistic networks including a Multi-Observation Hidden Markov Model (MOHMM), a Parallel Hidden Markov Model (PaHMM) and a Coupled Hidden Markov Model (CHMM). First online version published in February, 2006     

Generic abstract interpretation algorithms for Prolog: Two optimization techniques and their experimental evaluation

The efficient implementation of generic abstract interpretation algorithms for Prolog is reconsidered after References 1 and 2. Two new optimization techniques are proposed and applied to the original algorithm of Reference 1: dependency on clause prefixes and caching of operations. The first improvement avoids re-evaluating a clause prefix when no abstract value which it depends on has been updated. The second improvement consists of caching all operations on substitutions and reusing the results whenever possible. The algorithm and the two optimization techniques have been implemented in C (about 8000 lines of code each), tested on a large number of Prolog programs, and compared with the original implementation on an abstract domain containing modes, types and sharing. In conjunction with refinements of the domain algorithms, they produce an average reduction of more than 58 per cent is computation time. Extensive experimental results on the programs are given, including computation times, memory consumption, hit ratios for the caches, the number of operations performed, and the time distribution. As a main result, the improved algorithms exhibit the same efficiency as the specific tools of References 3 and 4, despite the fact that our abstract domain is more sophisticated and accurate. The abstract operations also take 90 per cent of the computation time, indicating that the overhead of the control is very limited. Results on a simpler domain are also given and show that even extremely basic domains can benefit from the optimizations. The general-purpose character of the optimizations is also discussed.     

Lloyd on intrinsic natural representation in simple mechanical minds

In Simple Minds, Dan Lloyd presents a reductive account of naturally representing machines. The theory entails that a system represents an event by virtue of potentially misrepresenting it whenever the machine satisfies a multiple information channel, convergence, and uptake condition. I argue that Lloyd's conditions are insufficient for systems intrinsically naturally to misrepresent, and hence insufficient for them intrinsically naturally to represent. The appearance of potential misrepresentation in such machines is achieved only by reference to the extrinsic design or extrinsic interpretation or attribution of an intrinsically nonexistent or underdetermined purpose, end, or goal to such devices in identifying an intended object of representation in the system's salient behavior under the uptake condition. The implication is that Lloyd-representation is not intrinsic natural representation in a cognitively relevant sense, and Lloyd's simple minds are not minds but mere machines.     

Indeterminancy in Recent Theories of Content

Jerry Fodor has charged that Fred Dretske's account of content suffers from indeterminacy to the extent that we should reject it in favor of Fodors own account. In this paper, we ask what the problem of indeterminacy really is; we distinguish a relatively minor problem we call looseness of fit from a major problem of failing to show how to point to what is not there. We sketch Dretske's account of content and how it is supposed to solve the major problem. After presenting Fodor's challenge as the claim that Dretske has failed to solve the major problem, we articulate a response available to Dretske. Although we do not think the response is ultimately successful, we argue that it is every bit as good as the response Fodor has offered to a similar challenge, in his so-called mixed theory. The upshot is this: despite advertisements to the contrary, Fodor's theory, in its mixed version, offers no real advantages over Dretske's regarding the serious problem of indeterminacy.     

PSE: An Object-Oriented Representation for Modeling and Managing Part-of Relationships

Part-of relationships are needed to model structures of composite objects. Especially, composite objects, known as physical assemblies, are very popular in several applications. The existing object-oriented representations of physical assemblies and query languages based on them have several disadvantages. For example, they suppose that the user knows the structure of a physical assembly completely. This is not a realistic requirement in those cases where a physical assembly consists of a huge number of parts. Likewise the user has in them to control traversals of physical assemblies, i.e. they have a low degree of declarativity. Further, they do not support intensional and combined extensional-intensional queries which would be necessary in an advanced manipulation of physical assemblies. We develop the PSE (Part-of Structure Element) representation for removing the above disadvantages. The PSE representation is a set theoretical and constructor-oriented representation for physical assemblies and it can be used in the implementation based on both the object-oriented and deductive object-oriented database (DOOD) paradigms.