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).

The paper is a overview of the major qualitative spatial representation and reasoning techniques. We survey the main aspects of the representation of qualitative knowledge including ontological aspects, topology, distance, orientation and shape. We also consider qualitative spatial reasoning including reasoning about spatial change. Finally there is a discussion of theoretical results and a glimpse of future work. The paper is a revised and condensed version of [33, 34].

Abstract. Open Mind Common Sense is a knowledge acquisition system designed to acquire commonsense knowledge from the general public over the web. We describe and evaluate our first fielded system, which enabled the construction of a 450,000 assertion commonsense knowledge base. We then discuss how our second-generation system addresses weaknesses discovered in the first. The new system acquires facts, descriptions, and stories by allowing participants to construct and fill in natural language templates. It employs word-sense disambiguation and methods of clarifying entered knowledge, analogical inference to provide feedback, and allows participants to validate knowledge and in turn each other. 1

Mereological and topological notions of connection, part, interior and complement are central to spatial reasoning and to the semantics of natural language expressions concerning locations and relative positions. While several authors have proposed axioms for these notions, no one with the exception of Tarski [18], who based his axiomatization of mereological notions on a Euclidean metric, has attempted to give them a semantics. We offer an alternative to Tarski, starting with mereotopological notions that have proved useful in the semantic analysis of spatial expressions. We also give a complete axiomatization of this account of mereotopological reasoning. 1

Relationships between concepts account for a large proportion of semantic knowledge. We present a nonparametric Bayesian model that discovers systems of related concepts. Given data involving several sets of entities, our model discovers the kinds of entities in each set and the relations between kinds that are possible or likely. We apply our approach to four problems: clustering objects and features, learning ontologies, discovering kinship systems, and discovering structure in political data. Philosophers, psychologists and computer scientists have proposed that semantic knowledge is best understood as a system of relations. Two questions immediately arise: how can these systems be represented, and how are these representations acquired? Researchers who start with the

A framework for the qualitative representation of positional information in a two-dimensional space is presented. Qualitative representations use discrete quantity spaces, where a particular distinction is introduced only if it is relevant to the context being modeled. This allows us to build a flexible framework that accommodates various levels of granularity and scales of reasoning. Knowledge about position in large-scale space is commonly represented by a combination of orientation and distance relations, which we express in a particular frame of reference between a primary object and a reference object. While the representation of orientation comes out to be more straightforward, the model for distances requires that qualitative distance symbols be mapped to geometric intervals in order to be compared; this is done by defining structure relations that are able to handle, among others, order of magnitude relations; the frame of reference with its three components (distance system, s...

A narrative is a course of real events about which we might have incomplete information. Formalisms for reasoning about action may be broadly divided into those which are narrativebased, such as the Event Calculus of Kowalski and Sergot, and those which reason on the level of hypothetical sequences of actions, in particular the Situation Calculus. This paper bridges the gap between these types of formalism by supplying a technique for linking incomplete narrative descriptions to Situation Calculus domain formulae written in the usual style using a Result function. Particular attention is given to actions with duration and overlapping actions. By illuminating the relationship between these two different styles of representation, the paper moves us one step closer to a full understanding of the space of all possible formalisms for reasoning about action. Introduction The Situation Calculus [15] is one of A.I.'s oldest and best understood formalisms for representing change, but it has of...

ion-Partitioned Evaluator (APE) architecture which has been tested in a simulated, single-agent, indoor navigation domain [SH90]. The APE architecture is composed of a number of concurrent, hierarchically abstract action control layers, each representing and reasoning about some particular aspect of the agent's task domain. Implemented as a parallel blackboard-based planner, the five layers --- sensor/motor, spatial, temporal, causal, and conventional (general knowledge) --- effectively partition the agent's data processing duties along a number of dimensions including temporal granularity, information/resource use, and functional abstraction. Perceptual information flows strictly from the agent sensors (connected to the sensor /motor level) toward the higher levels, while command or goal-achievement information flows strictly downward towards the agent's effectors (also connected to the sensor/motor level). Besides mechanisms for communicating with other layers, each layer in the AP...

Abstract. There is a strong argument in Artificial Intelligence in Education which advocates that computer-based learning systems need to adapt to the needs of learners if they are to provide for effective personalised instruction (Self, 1999a). Diagnosing a learner's cognitive capacity is a crucial issue in building adaptive systems. We have explored an interactive open learner modelling (IOLM) approach which conceives diagnosis as an interactive process involving both a computer system and a learner that discuss and together construct the learner model. This paper outlines the architecture of an interactive open learner modelling system and illustrates the method in a terminological domain. We discuss an evaluative study of an IOLM demonstrator – a system called STyLE-OLM. The results from the study demonstrate potential benefits of the method for improving the quality of the learner model and providing a means for fostering reflective thinking. We argue that IOLM is a fruitful approach which may be employed in intelligent learning environments both for obtaining a better model of a learner’s cognitive state and engaging learners in reflective activities.

Although most people believe that planners that delay step-ordering decisions as long as possible are more efficient than those that manipulate totally ordered sequences of actions, this intuition has received little formal justification or empirical validation. In this paper we do both, characterizing the types of domains that offer performance differentiation and the features that distinguish the relative overhead of three planning algorithms. As expected, the partial-order (nonlinear) planner often has an advantage when confronted with problems in which the specific order of the plan steps is critical. We argue that the observed performance differences are best understood with an extension of Korf's taxonomy of subgoal collections. Each planner quickly solved problems whose subgoals were independent or trivially serializable, but problems with laboriously serializable or nonserializable subgoals were intractable for all planners. Since different plan representations induce distinct ...