Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques

In this paper, we propose an unsupervised neural network allowing a robot to learn sensory-motor associations with a delayed reward. The robot task is to learn the “meaning” of pictograms in order to “survive” in a maze. First, we introduce a new neural conditioning rule probabilistic conditioning rule (PCR) allowing us to test hypotheses (associations between visual categories and movements) during a given time span. Second, we describe a real maze experiment with our mobile robot. We propose a neural architecture overcoming the difficulty to build visual categories dynamically while associating them to movements. Third, we propose to use our algorithm on a simulation in order to test it exhaustively. We give the results for different kinds of mazes and we compare our system to an adapted version of the Q-learning algorithm. Finally, we conclude by showing the limitations of approaches that do not take into account the intrinsic complexity of a reasoning based on image recognition.     

Aggregation of a class of interconnected, linear dynamical systems

In this paper, we introduce the notion of a “meaningful” average of a collection of dynamical systems as distinct from an “ensemble” average. Such a notion is useful for the study of a variety of dynamical systems such as traffic flow, power systems, and econometric systems. We also address the associated issue of the existence and computation of such an average for a class of interconnected, linear, time invariant dynamical systems. Such an “average” dynamical system is not only attractive from a computational perspective, but also represents the average behavior of the interconnected dynamical systems. The problem of analysis and control of heirarchical, large scale control systems can be simplified by approximating the lower level dynamics of such systems with such an average dynamical system.     

Sketching space

In this paper, we present a sketch modelling system which we call Stilton. The program resembles a desktop VRML browser, allowing a user to navigate a three-dimensional model in a perspective projection, or panoramic photographs, which the program maps onto the scene as a “floor” and “walls”. We place an imaginary two-dimensional drawing plane in front of the user, and any geometric information that user sketches onto this plane may be reconstructed to form solid objects through an optimisation process. We show how the system can be used to reconstruct geometry from panoramic images, or to add new objects to an existing model. While panoramic imaging can greatly assist with some aspects of site familiarisation and qualitative assessment of a site, without the addition of some foreground geometry they offer only limited utility in a design context. Therefore, we suggest that the system may be of use in “just-in-time” CAD recovery of complex environments, such as shop floors, or construction sites, by recovering objects through sketched overlays, where other methods such as automatic line-retrieval may be impossible. The result of using the system in this manner is the “sketching of space” — sketching out a volume around the user — and once the geometry has been recovered, the designer is free to quickly sketch design ideas into the newly constructed context, or analyse the space around them. Although end-user trials have not, as yet, been undertaken we believe that this implementation may afford a user-interface that is both accessible and robust, and that the rapid growth of pen-computing devices will further stimulate activity in this area.     

Importance in knowledge systems

In knowledge systems, pieces of information (evidence, hypotheses, attributes, terms, documents, rules) are usually assumed to carry equal importance and to be independent of each other, although it might not actually be the case. Issues for a logic of weighted queries, with possibility of also weighting documents and logical connectors (in terms of intelligent retrieval, for example) are presented here, using “min” or t-norms, and soft operators involving p-norms. This logic cannot be a conventional one for, when introducing relative importance between concepts, definitions are different for ANDed and ORed weighted queries. A concept of “nought”, a limit case of no-importance queries, and its behaviour with fuzzy sets operations is developed, in particular the notion of an extended membership is introduced. Finally it is shown, with a biomedical example, how to combine importance with soft matching in rule-based systems.     

FQUERY III+: A “human-consistent” database querying system based on fuzzy logic with linguistic quantifiers

Using a fuzzy-logic-based calculus of linguistically quantified propositions we present FQUERY III+, a new, more “human-friendly” and easier-to-use implementation of a querying scheme proposed originally by Kacprzyk and Zio kowski to handle imprecise queries including a linguistic quantifier as, e.g. find all records in which most (almost all, much more than 75%, … or any other linguistic quantifier) of the important attributes (out of a specified set) are as desired (e.g. equal to five, more than 10, large, more or less equal to 15, etc.). FQUERY III+ is an “add-on” to Ashton-Tate's dBase III Plus.     

A note on web intelligence, world knowledge and fuzzy logic

Existing search engines––with Google at the top––have many remarkable capabilities; but what is not among them is deduction capability––the capability to synthesize an answer to a query from bodies of information which reside in various parts of the knowledge base.

In recent years, impressive progress has been made in enhancing performance of search engines through the use of methods based on bivalent logic and bivalent-logic-based probability theory. But can such methods be used to add nontrivial deduction capability to search engines, that is, to upgrade search engines to question-answering systems? A view which is articulated in this note is that the answer is “No.” The problem is rooted in the nature of world knowledge, the kind of knowledge that humans acquire through experience and education.

It is widely recognized that world knowledge plays an essential role in assessment of relevance, summarization, search and deduction. But a basic issue which is not addressed is that much of world knowledge is perception-based, e.g., “it is hard to find parking in Paris,” “most professors are not rich,” and “it is unlikely to rain in midsummer in San Francisco.” The problem is that (a) perception-based information is intrinsically fuzzy; and (b) bivalent logic is intrinsically unsuited to deal with fuzziness and partial truth.

To come to grips with fuzziness of world knowledge, new tools are needed. The principal new tool––a tool which is briefly described in this note––is Precisiated Natural Language (PNL). PNL is based on fuzzy logic and has the capability to deal with partiality of certainty, partiality of possibility and partiality of truth. These are the capabilities that are needed to be able to draw on world knowledge for assessment of relevance, and for summarization, search and deduction.     

On the observability codistributions of a nonlinear system

The purpose of this is to introduce the notion of observability codistribution for a nonlinear system, which extends the (dual of the) notion of “unobservability subspace”. Then, we study its properties, which bear important similarities with a number of properties which render the notion of unobservability subspace powerful in the solution of certain design problems.     

A structure for expert systems maintenance

An approach to the design of maintainable expert systems is presented. Central to this approach is a conceptual model in which the data and knowledge are both modelled as formal “items” in a uniform way. “Objects” are introduced as “item building” operators. The notion of the “decomposition” of items and objects provides the foundation for a single rule of normalization. This single rule applies to all items and objects, including knowledge items, and is a non-trivial generalization of the traditional normal forms for database. Coupling relationships represent the necessary maintenance paths in the conceptual model. A complete characterization of coupling relationships is given, and the value of normalization to the reduction of maintenance costs is discussed.     

On goal-directed provability in classical logic

One of the key features of logic programming is the notion of goal-directed provability. In intuitionistic logic, the notion of uniform proof has been used as a proof-theoretic characterization of this property. Whilst the connections between intuitionistic logic and computation are well known, there is no reason per se why a similar notion cannot be given in classical logic. In this paper we show that there are two notions of goal-directed proof in classical logic, both of which are suitably weaker than that for intuitionistic logic. We show the completeness of this class of proofs for certain fragments, which thus form logic programming languages. As there are more possible variations on the notion of goal-directed provability in classical logic, there is a greater diversity of classical logic programming languages than intuitionistic ones. In particular, we show how logic programs may contain disjunctions in this setting. This provides a proof-theoretic basis for disjunctive logic programs, as well as characterising the “disjunctive” nature of answer substitutions for such programs in terms of the provability properties of the classical connectives Λ and Λ.     

Approximate greatest common divisor of many polynomials, generalised resultants, and strength of approximation

The computation of the greatest common divisor (GCD) of many polynomials is a nongeneric problem. Techniques defining “approximate GCD” solutions have been defined, but the proper definition of the “approximate” GCD, and the way we can measure the strength of the approximation has remained open. This paper uses recent results on the representation of the GCD of many polynomials, in terms of factorisation of generalised resultants, to define the notion of “approximate GCD” and define the strength of any given approximation by solving an optimisation problem. The newly established framework is used to evaluate the performance of alternative procedures which have been used for defining approximate GCDs.     

On the feasibility of IEEE 802.11 multi-channel multi-hop mesh networks

Several scientific works have considered the possibility to build Wireless Mesh Networks (WMN) using multi-channel IEEE 802.11 architectures. At the basis of these works is the notion of “non-overlapping” channels, i.e. with a frequency separation equal or greater than 25 MHz. It is now a common assumption that multiple independent transmissions over these channels can coexist without mutual interference even in physical proximity. In this work we demonstrate that this assumption does not hold in general. Through an extensive set of experiments we illustrate the presence of cross-channel interference between “non-overlapping” channels at relay nodes due to the “near-far” effect. We analyze in what manner the MAC layer reacts to such an interference and how this problem extends to higher layers, with detrimental effects on the global throughput. The central problem is that cross-channel interference is not handled adequately by the MAC layer, and in some cases single-channel multi-hop settings perform better than multi-channel. Our results highlight a serious mismatch between some routing and channel assignment schemes proposed recently by the research community, assuming full separation between non-overlapping channels, and what is achievable in practice. More generally, as the presence of cross-channel interference can not be neglected at relay nodes, our findings point to a fundamental problem in building Multi-channel Multi-hop WMN based on IEEE 802.11b/g technology.     

“Chaotic relaxation” in concurrently asynchronous neural networks

In this paper we analyze a fundamental issue which directly impacts the scalability of current theoretical neural network models to applicative embodiments, in both software as well as hardware. This pertains to the inherent and unavoidable concurrent asynchronicity of emerging fine-grained computational ensembles and the consequent chaotic manifestations in the absence of proper conditioning. The latter concern is particularly significant since the computational inertia of neural networks in general and our dynamical learning formalisms manifests itself substantially, only in massively parallel hardward—optical, VLSI or opto-electronic. We introduce a mathematical framework for systematically reconditioning additive-type models and derive a neuro-operator, based on the chaotic relaxation paradigm whose resulting dynamics are neither “concurrently” synchronous nor “sequentially” asynchronous. Necessary and sufficient conditions guaranteeing concurrent asynchronous convergence are established in terms of contracting operators. Lyapunov exponents are also computed to characterize the network dynamics and to ensure that throughput-limiting “emergent computational chaos” behavior in models reconditioned with concurrently asynchronous algorithms was eliminated.     

Markov Properties and Factorization of Possibility Distributions

Markov properties and factorization are powerful tools allowing the expression of multidimensional probability distributions by means of low-dimensional ones. As multidimensional possibilistic models have been studied for several years, the demand for analogous tools in possibility theory seems quite natural. This paper is intended to be a promotion of De Cooman's measure-theoretic approach to possibility theory, as this approach allows us to find analogies to many important results obtained in a probabilistic framework.First we recall our definition of conditional possibilistic independence, parameterized by a continuous t-norm, and its properties. Then we introduce Markov properties, based on this conditional independence notion, and factorization of possibility distributions (again parameterized by a continuous t-norm) and we find the relationships between them. Our results are accompanied by a number of counterexamples, which show that the assumptions of particular theorems are substantial.     

An exponential membership function for fuzzy multiple objective linear programming

The operator “min” is one of the most frequently used aggregation operators in fuzzy decision. However, this operator is the softest operator and no allowance is made for any compensation. The “product” and other operators, some of them may be compensatory, are seldom used because of the nonlinearity of the resulting problem. In this paper, an exponential, instead of linear membership function is proposed. The advantages of using exponential membership are two fold. First, the resulting problems can be transformed to linear ones when the “product” and several other nonlinear aggregate operators are used. Secondly, exponential representation is more realistic than the linear ones usually used for some practical applications.     

Similarity-based ranking and query processing in multimedia databases

Since media-based evaluation yields similarity values, results to a multimedia database query, Q(Y₁,…,Y_n), is defined as an ordered list S_Q of n-tuples of the form X₁,…,X_n. The query Q itself is composed of a set of fuzzy and crisp predicates, constants, variables, and conjunction, disjunction, and negation operators. Since many multimedia applications require partial matches, S_Q includes results which do not satisfy all predicates. Due to the ranking and partial match requirements, traditional query processing techniques do not apply to multimedia databases. In this paper, we first focus on the problem of “given a multimedia query which consists of multiple fuzzy and crisp predicates, providing the user with a meaningful final ranking”. More specifically, we study the problem of merging similarity values in queries with multiple fuzzy predicates. We describe the essential multimedia retrieval semantics, compare these with the known approaches, and propose a semantics which captures the requirements of multimedia retrieval problem. We then build on these results in answering the related problem of “given a multimedia query which consists of multiple fuzzy and crisp predicates, finding an efficient way to process the query.” We develop an algorithm to efficiently process queries with unordered fuzzy predicates (sub-queries). Although this algorithm can work with different fuzzy semantics, it benefits from the statistical properties of the semantics proposed in this paper. We also present experimental results for evaluating the proposed algorithm in terms of quality of results and search space reduction.     

Comparison of two inference methods for P-type fuzzy logic control through experimental investigation using a hydraulic manipulator

This paper presents a comparison of the two important inference schemes: “individual-rule-based inference” and “compositional rule of inference” as applied to fuzzy logic control, through experimental investigation. The techniques are implemented on a hydraulic manipulator of an industrial machine with P-type fuzzy control. The fuzzy logic controller is designed for automatic positioning of the cutter blade of an automated fish-cutting machine. The features of the machine, which uses hydraulic servo control for cutter positioning, are outlined. The performance of the machine under the two inference schemes is examined and contrasted. Some practical implementations of the results are indicated.     

Towards a theory of delegation for agent-based systems

In this paper a theory of delegation is presented. There are at least three reasons for developing such a theory. First, one of the most relevant notions of “agent” is based on the notion of “task” and of “on behalf of”. In order to found this notion a theory of delegation among agents is needed. Second, the notion of autonomy should be based on different kinds and levels of delegation. Third, the entire theory of cooperation and collaboration requires the definition of the two complementary attitudes of goal delegation and adoption linking collaborating agents.

After motivating the necessity for a principled theory of delegation (and adoption) the paper presents a plan-based approach to this theory. We analyze several dimensions of the delegation/adoption (on the basis of the interaction between the agents, of the specification of the task, of the possibility to subdelegate, of the delegation of the control, of the help levels). The agent's autonomy and levels of agency are then deduced. We describe the modelling of the client from the contractor's point of view and vice versa, with their differences, and the notion of trust that directly derives from this modelling.

Finally, a series of possible conflicts between client and contractor are considered: in particular collaborative conflicts, which stem from the contractor's intention to help the client beyond its request or delegation and to exploit its own knowledge and intelligence (reasoning, problem solving, planning, and decision skills) for the client itself.     

Finite fuzzy automata, regular fuzzy languages, and pattern recognition

Fuzzy set theory is summarized, and finite fuzzy automata and languages are described. Some results in fuzzy languages based on the “max(min)” rule are discussed. An application of fuzzy languages in syntactic pattern analysis is described and is illustrated by a simple example.     

FILIP: A fuzzy intelligent information system with learning capabilities

FILIP (fuzzy intelligent learning information processing) system is designed with the goal to model human information processing. The issues addressed are uncertain knowledge representation and approximate reasoning based on fuzzy set theory, and knowledge acquisition by “being told” or by “learning from examples”. Concepts that can be “learned” by the system can be imprecise (fuzzy), or the knowledge can be incomplete. In the latter case, FILIP uses the concept of similarity to extrapolate the knowledge to cases that were not covered by examples provided by the user. Concepts are stored in the Knowledge Base and employed in intelligent query processing, based on flexible inference that supports approximate matches between the data in the database and the query.

The architecture of FILIP is discussed, the learning algorithm is described, and examples of the system's performance in the knowledge acquisition and querying modes, together with its explanatory capabilities are shown.