Maher, Mary Lou


Deep Learning for Identifying Potential Conceptual Shifts for Co-creative Drawing

arXiv.org Machine Learning

We present a system for identifying conceptual shifts between visual categories, which will form the basis for a co-creative drawing system to help users draw more creative sketches. The system recognizes human sketches and matches them to structurally similar sketches from categories to which they do not belong. This would allow a co-creative drawing system to produce an ambiguous sketch that blends features from both categories.


Surprise-Triggered Reformulation of Design Goals

AAAI Conferences

This paper presents a cognitive model of goal formulation in designing that is triggered by surprise. Cognitive system approaches to design synthesis focus on generating alternative designs in response to design goals or requirements. Few existing systems provide models for how goals change during designing, a hallmark of creative design in humans. In this paper we present models of surprise and reformulation as metacognitive processes that transform design goals in order to explore surprising regions of a design search space. The model provides a system with specific goals for exploratory behaviour, whereas previous systems have modelled exploration and novelty-seeking abstractly. We use observed designs to construct a probabilistic model that represents expectations about the design domain, and then reason about the unexpectedness of new designs with that model. We implement our model in the domain of culinary creativity, and demonstrate how the cognitive behaviors of surprise and problem reformulation can be incorporated into design reasoning.


Using Computational Creativity to Guide Data-intensive Scientific Discovery

AAAI Conferences

The generation of plausible hypotheses from observations is a creative process.  Scientists looking to explain phenomena must invent hypothetical relationships between their dependent and independent variables and then design methods to verify or falsify them. Data-driven science is expanding both the role of artificial intelligence in this process and the scale of the observations from which hypotheses must be abduced. We adopt methods from the field of computational creativity -- which seeks to model and understand creative behaviour -- to the generation of scientific hypotheses.  We argue that the generation of new insights from data is a creative process, and that a search for new hypotheses can be guided by evaluating those insights as creative artefacts. We present a framework for data-driven hypothesis discovery that is based on a computational model of creativity evaluation.


The Role of AI in Wisdom of the Crowds for the Social Construction of Knowledge on Sustainability

AAAI Conferences

One of the original applications of crowdsourcing the construction of knowledge is Wikipedia, which relies entirely on people to contribute, extend, and modify the representation of knowledge. This paper presents a case for combining AI and wisdom of the crowds for the social construction of knowledge. Our social-computational approach to collective intelligence combines the strengths of human cognitive diversity in producing content and the capabilities of an AI, through methods such as topic modeling, to link and synthesize across these human contributions. In addition to drawing from established domains such as Wikipedia for inspiration and guidance, we present the design of a system that incorporates AI into wisdom of the crowds to develop a knowledge base on sustainability. In this setting the AI plays the role of scholar, as might many of the other participants, drawing connections and synthesizing across contributions. We close with a general discussion, speculating on educational implications and other roles that an AI can play within an otherwise collective human intelligence.


Reasoning in the Absence of Goals

AAAI Conferences

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.


Reports of the AAAI 2011 Spring Symposia

AI Magazine

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2011 Spring Symposium Series Monday through Wednesday, March 21–23, 2011 at Stanford University. The titles of the eight symposia were AI and Health Communication, Artificial Intelligence and Sustainable Design, AI for Business Agility, Computational Physiology, Help Me Help You: Bridging the Gaps in Human-Agent Collaboration, Logical Formalizations of Commonsense Reasoning, Multirobot Systems and Physical Data Structures, and Modeling Complex Adaptive Systems As If They Were Voting Processes.


Reports of the AAAI 2011 Spring Symposia

AI Magazine

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University’s Department of Computer Science, presented the 2011 Spring Symposium Series Monday through Wednesday, March 21–23, 2011 at Stanford University. The titles of the eight symposia were AI and Health Communication, Artificial Intelligence and Sustainable Design, AI for Business Agility, Computational Physiology, Help Me Help You: Bridging the Gaps in Human-Agent Collaboration, Logical Formalizations of Commonsense Reasoning, Multirobot Systems and Physical Data Structures, and Modeling Complex Adaptive Systems As If They Were Voting Processes. This report summarizes the eight symposia.



Towards Grammars for Cradle-to-Cradle Design

AAAI Conferences

Cradle to cradle design (C2C) considers material use and reuse as integral to the composition and specification of the design. Since grammars have been used extensively to model design processes, we consider the possibilities of grammars to model C2C design. Central to our proposal is the idea that products cannot be designed in isolation, but C2C desiderata can only be achieved by explicit design of families of products that share material reuse possibilities. Grammars, heuristic search and various forms of machine learning are highlighted as critical in grappling with the complexities of C2C design.