15,151 research outputs found
A canonical theory of dynamic decision-making
Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering
Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands
To understand diverse natural language commands, virtual assistants today are
trained with numerous labor-intensive, manually annotated sentences. This paper
presents a methodology and the Genie toolkit that can handle new compound
commands with significantly less manual effort. We advocate formalizing the
capability of virtual assistants with a Virtual Assistant Programming Language
(VAPL) and using a neural semantic parser to translate natural language into
VAPL code. Genie needs only a small realistic set of input sentences for
validating the neural model. Developers write templates to synthesize data;
Genie uses crowdsourced paraphrases and data augmentation, along with the
synthesized data, to train a semantic parser. We also propose design principles
that make VAPL languages amenable to natural language translation. We apply
these principles to revise ThingTalk, the language used by the Almond virtual
assistant. We use Genie to build the first semantic parser that can support
compound virtual assistants commands with unquoted free-form parameters. Genie
achieves a 62% accuracy on realistic user inputs. We demonstrate Genie's
generality by showing a 19% and 31% improvement over the previous state of the
art on a music skill, aggregate functions, and access control.Comment: To appear in PLDI 201
Rethinking affordance
n/a – Critical survey essay retheorising the concept of 'affordance' in digital media context. Lead article in a special issue on the topic, co-edited by the authors for the journal Media Theory
Advances towards a General-Purpose Societal-Scale Human-Collective Problem-Solving Engine
Human collective intelligence has proved itself as an important factor in a
society's ability to accomplish large-scale behavioral feats. As societies have
grown in population-size, individuals have seen a decrease in their ability to
activeily participate in the problem-solving processes of the group.
Representative decision-making structures have been used as a modern solution
to society's inadequate information-processing infrastructure. With computer
and network technologies being further embedded within the fabric of society,
the implementation of a general-purpose societal-scale human-collective
problem-solving engine is envisioned as a means of furthering the
collective-intelligence potential of society. This paper provides both a novel
framework for creating collective intelligence systems and a method for
implementing a representative and expertise system based on social-network
theory.Comment: Collective Problem Solving Theory and Social-Network algorith
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