Human wayfinding operates very very differently from traditional deterministic algorithms owing to a) restrictions in working memory resulting in subjective regionalized maps, and b)flexible adoption of different navigation strategies. While a number of cognitive strategies have been proposed for human wayfinding, these have been hard to evaluate thoroughly owing to a lack of computational simulation. In this work, we propose a stochastic approach for capturing these aspects, and argue for a memoryless, stationary implementation. In two longitudinal experiments on the same group of subjects, we first estimate the subjective regionalized maps for each subject on the same familiar spatial domain. Later, based on their wayfinding responses, we can estimate the stationary probabilities for different strategies. We apply this algorithm to evaluate three wayfinding strategies proposed in the literature, and repudiate the previously held suggestion that they are followed equiprobably.

This paper discusses the challenge of designing instructable agents that can learn through interaction with a human expert. Learning through instruction is a powerful paradigm for acquiring knowledge because it limits the complexity of the learning task in a variety of ways. To support learning through instruction, the agent must be able to effectively communicate its lack of knowledge to the human, comprehend instructions, and apply them to the ongoing task. Weidentify some problems of concern when designing instructable agents. We propose an agent design that addresses some of these problems. We instantiate this design in the Soar cognitive architecture and analyze its capabilities on a learning task.

For intelligent agents modeled to emulate people, reference resolution is memory management: when processing an object or event – whether it appears in language or in the simulated physical or cognitive experience of the agent – the agent must determine how that object or event correlates with known objects and events, and must store the new memory with semantically explicit links to related prior knowledge. This paper discusses eventualities for memory-based reference resolution and the modeling strategies used in the OntoAgent environment to permit agents to fully and automatically make reference decisions.

The Odd One Out test of intelligence consists of 3x3 matrix reasoning problems organized in 20 levels of difficulty. Addressing problems on this test appears to require integration of multiple cognitive abilities usually associated with creativity, including visual encoding, similarity assessment, pattern detection, and analogical transfer. We describe a novel fractal strategy for addressing visual analogy problems on the Odd One Out test. In our strategy, the relationship between images is encoded fractally, capturing important aspects of similarity as well as inherent self-similarity. The strategy starts with fractal representations encoded at a high level of resolution, but, if that is not sufficient to resolve ambiguity, it automatically adjusts itself to the right level of resolution for addressing a given problem. Similarly, the strategy starts with searching for fractally-derived similarity between simpler relationships, but, if that is not sufficient to resolve ambiguity, it automatically shifts to search for such similarity between higher-order relationships.  We present preliminary results and initial analysis from applying the fractal technique on nearly 3,000 problems from the Odd One Out test.

Language processing architectures today are rarely designed to provide psychologically plausible accounts of their representations and algorithms. Engineering decisions dominate. This has led to words being seen as an incidental part of the architecture: the repository of all of language’s idiosyncratic aspects. Drawing on a body of past and ongoing research by myself and others I have concluded that this view of words is wrong. Words are actually present at the most abstract, pre-linguistic levels of the NLP architecture and that there are phenomena in language use that are best accounted for by assuming that concepts are words.

The literature on conceptual blending and metaphor-making has illustrations galore of how these mechanisms may support the creation and grounding of new concepts (or whole domains) in terms of a complex, integrated network of older ones. In spite of this, as of yet there is no general computational account of blending and metaphor-making that has proven powerful enough as to cover all the examples from the literature. This paper proposes a logic-based framework for blending and metaphor making and explores its applicability in settings as diverse as mathematical domain formation, classical rationality puzzles, and noun-noun combinations.

In creative industries such as design and research it is common to reason about ‘problem-finding’ before tasks or goals can be established. Problem-finding may also continue throughout the problem-solving process, so achieving goals may be an ongoing process of discovery as well as iterative improvement and refinement. This paper considers the design of cognitive systems with complementary processes for both problem-finding and problem-solving. We review a range of approaches that may complement goal-directed reasoning when an artificial system does not or cannot know precisely what it is looking for. We argue that there is a spectrum of approaches that can be used for reasoning in the absence of goals, which make progressively weaker assumptions about the definition and presence goals, and that goal-oriented behavior can be an intermediate result of problem-finding, rather than as a starting point for problem-solving. We demonstrate one such approach based on implicit motives and incentives.

In this paper we discuss an elaboration account of insight that provides answers to two of the main questions regarding insight problem solving: why insight problems are so difficult for humans and why insight is so rapid in nature. We claim that the difficulty in insight problems is due to misguided heuristic search and that this difficulty is overcome using a reformulation mechanism. Furthermore, we claim that search is carried out quickly when the heuristics are good--explaining the rapid nature of insight. We clarify our account by providing examples and initial empirical results. In conclusion, we review related work and discuss possible future work.

An important form of learning involves acquiring skills that let an agent achieve its goals. While there has been considerable work on learning in planning, most approaches have been sensitive to the representation of domain context, which hurts their generality. A learning mechanism that constructs skills effectively across different representations would suggest more robust behavior. In this paper, we present a novel approach to learning hierarchical task networks that acquires conceptual predicates as learning proceeds, making it less dependent on carefully crafted background knowledge. The representation acquisition procedure expands the system's knowledge about the world, and leads to more rapid learning. We show the effectiveness of the approach by comparing it with one that doesnot change domain representation.

The ability to delay intentions and remember them in the proper context is an important ability for general artificial agents. In this paper, we define the functional requirements of an agent capable of fulfilling delayed intentions with its long-term memories. We show that the long-term memories of different cognitive architec- tures share similar functional properties and that these mechanisms can be used to support delayed intentions. Finally, we do a preliminary evaluation of the different memories for fulfilling delayed intentions and show that there are trade-offs between memory types that warrant further research.