Continuant Occurrent ' rContinuant Occurrent Object Process Schema Script Juncture Participation Description History Structure Situation Reason Purpose Three-dimensional matrix of twelve of Sowa's categories (p. 75).

This chapter describes Parallel Distributed Genetic Programming (PDGP), a form of Genetic Programming (GP) which is suitable for the development of programs with a high degree of parallelism and an ecient and effective reuse of partial results. Programs are represented in PDGP as graphs with nodes representing functions and terminals, and links representing the flow of control and results. In the simplest form of PDGP links are directed and unlabelled, in which case PDGP can be considered a generalisation of standard GP. However, more complex representations can be used, which allow the exploration of a large space of possible programs including standard tree-like programs, logic networks, neural networks, recurrent transition networks, finite state automata, etc.

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rulebased reasoning and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modelling.

this paper contributes toward the goal of understanding how a computer can be programmed to learn by isolating features of incremental learning algorithms that theoretically enhance their learning potential. In particular, we examine the effects of imposing a limit on the amount of information that learning algorithm can hold in its memory as it attempts to This work was facilitated by an international agreement under NSF Grant 9119540.

The methods for the interpretation of aerial images and maps are usually different although both describe the same landscape. The presented work shows that regarding remote sensing data and maps as different kinds of sensors allows a similar approach for both in the domain of landscape interpretation. The prior knowledge about the landscape objects is represented explicitly by semantic nets. Based on the semantics of the network language a problem independent set of rules controls the scene interpretation. Important is that the scene analysis employs a partial interpretation derived from a Digital Landscape Model (ATKIS DLM 25/1). This partial interpretation is used to generate an initial scene description. Consecutively the scene description is verified in aerial images and maps. Interpretation proceeds iteratively mixing top-down and bottom-up strategies. This paper shows the representation of the knowledge in several semantic layers, the strategies of interpretation, and the methods to interprete aerial images and maps using a DLM as partial interpretation. 1

Contextual vocabulary acquisition (CVA) is the active, deliberate acquisition of a meaning for an unknown word in a text by reasoning from textual clues, prior knowledge, and hypotheses developed from prior encounters with the word, but without external sources of help such as dictionaries or people. Published strategies for doing CVA vaguely and unhelpfully tell the reader to “guess”. AI algorithms for CVA can fill in the details that replace “guessing ” by “computing”; these details can then be converted to a curriculum that can be taught to students to improve their reading comprehension. Such algorithms also suggest a way out of the Chinese Room and show how holistic semantics can withstand certain objections. 1 1 Computational Philosophy and Philosophical Computation Computer science in general, and AI in particular, have a lot to give to philosophy, and vice versa, as Daniel Dennett once noted (1978: 126; cf. Rapaport 1986b). This essay discusses an interdisciplinary, applied cognitive-science research project that exhibits how philosophy can influence AI, how AI can influence philosophy, and how both can influence educational practice. I 1 take “computational philosophy ” to be the application of computational (i.e., algorithmic) solutions to philosophical problems. 2 An example from my own research would be the use of the SNePS knowledgerepresentation,

Abstract 2 Most autonomous agents are situated in a social context and need to interact with other agents (both human and artificial) to complete their problem solving objectives. Such agents are usually capable of performing a wide range of actions and engaging in a variety of social interactions. Faced with this variety of options, an agent must decide what to do. There are many potential decision making functions that could be employed to make the choice. Each such function will have a different effect on the success of the individual agent and of the overall system in which it is situated. To this end, this thesis examines agents ’ decision making functions to ascertain their likely properties and attributes. A novel framework for characterising social decision making is presented which provides explicit reasoning about the potential benefits of the individual agent, particular sub-groups of agents or the overall system. This framework enables multi-farious social interaction attitudes to be identified and defined; ranging from the purely self-interested to the purely altruistic. In particular, however, the focus is on the spectrum of socially responsible agent behaviours in which agents attempt to balance their own needs with those of the overall system. Such behaviour aims to ensure that both the agent and the overall system perform well.

Abstract. This article surveys and gives historical accounts to the algorithmic essentials of directed model checking, a promising bug-hunting technique to mitigate the state explosion problem. In the enumeration process, successor selection is prioritized. We discuss existing guidance and methods to automatically generate them by exploiting system abstractions. We extend the algorithms to feature partial-order reduction and show how liveness problems can be adapted by lifting the search space. For deterministic, finite domains we instantiate the algorithms to directed symbolic, external and distributed search. For real-time domains we discuss the adaption of the algorithms to timed automata and for probabilistic domains we show the application to counterexample generation. Last but not least, we explain how directed model checking helps to accelerate finding solutions to scheduling problems. 1

knowledge based approach for automatic generation of 3D-landscape models from aerial images is presented. The use of models for visualization tasks results in two requirements: efficient representation and high realism. Efficient representation of 3D-geometry is achieved by polygon meshes. Realism requires that the models meet the expectations of a human observer, who knows e.g. that roads are planar and forest edges possess a height step. The presented knowledge based modeler AID/l employs prior knowledge about the appearance of the objects in the scene to derive object specific constraints for surface reconstruction and to complete partially occluded objects. This requires an image interpretation to assign a semantic to the scene objects. The knowledge is represented explicitly by semantic nets and rules.

Computationalism, the notion that cognition is computation, is a working hypothesis of many AI researchers and Cognitive Scientists. Although it has not been proved, neither has it been disproved. In this paper, I give some refutations to some well-known alleged refutations of computationalism. My arguments have two themes: people are more limited than is often recognized in these debates; computer systems are more complicated than is often recognized in these debates. To underline the latter point, I sketch the design and abilities of a possible embodied computer system. 1 Artificial Intelligence and Computationalism There are several disparate goals pursued by Artificial Intelligence (AI) researchers: computational psychology, computational philosophy, and advanced Computer Science [Shapiro, 1992]. In this paper, I will concentrate on computational philosophy, which could also be called "the computational study of cognition." AI is often thought of in the popular press as a technolo...