Representations of commonsense knowledge

By Ernest Davis. 2014

532 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds (2017, background): “Logical AI has a long and distinguished history in dealing with thorny topics in commonsense reasoning [Davis, 2014]; integrating these with probabilistic models may prove fruitful.”
Towards Deep Symbolic Reinforcement Learning (2016, background): “But the everyday physical world is structured according to many other common sense priors [28, 27, 29, 25, 30].”
“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding (2019, background): “Natural language understanding requires the ability to reason with commonsense knowledge (Schu-bert, 2002; Davis, 2014), and the last few years have seen signiﬁcant amount of work in this direction (e.g., Zhang et al. (2017); Bauer et al. (2018); Tandon et al. (2018)).”
Understanding script-based stories using commonsense reasoning (2004, background): “Axioms DEC5 through DEC8 are frame axioms that resemble explanation closure frame axioms (Haas, 1987; Schubert, 1990; Davis, 1990; Reiter, 1991; Reiter, 2001), extended to allow fluents to be released from the commonsense law of inertia.”
CIDER: Commonsense Inference for Dialogue Explanation and Reasoning (2021, background): “This is similar to deductive commonsense reasoning (Davis, 2014).”
Open Mind Common Sense: Knowledge Acquisition from the General Public (2002, background): “What is holding back the development of such applications? While there has been much work on developing representations and reasoning methods for commonsense domains (Davis 1990), and on the logical underpinnings for commonsense reasoning (Shanahan 1997), there has been far less work on finding ways”
MEBN: A language for first-order Bayesian knowledge bases (2008, background): “Davis [17] defines a logic as a schema for defining languages to describe and reason about entities in different domains of application.”
A New Direction in AI: Toward a Computational Theory of Perceptions (2001, methodology): “Within AI, there are methodologies, prominent among which are qualitative reasoning and qualitative process theory (QPT) (Sun 1994; Raiman 1991; Davis 1990; de Kleer and Bobrow 1984; Forbus 1984; Kuipers 1984), that provide effective tools for dealing with interval-valued types of perception-based…”
Qualitative Spatial Representation and Reasoning: An Overview (2001, unknown): “Some work [75, 49] insist more on the concept of dynamic processes; elsewhere [163, 109] there is research on default reasoning in metric spaces that are not really clearly characterized.”
The First Law of Robotics (A Call to Arms) (1994, unknown): “As we begin to deploy agents in environments where they can do some real damage, the time has come to revisit Asimov's Laws.”
The Winograd Schema Challenge and Reasoning about Correlation (2015, background): “This difficult task can benefit from existing work on formalizing commonsense knowledge (Davis 1990; Mueller 2006).”
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering (2019, background): “tational linguists, cognitive psychologists (see for instance (Davis, 2014)): at the high level, we can identify declarative commonsense, whose scope encompassess factual knowledge, e.”
Envisioning the qualitative effects of robot manipulation actions using simulation-based projections (2017, background): “A general overview on spatial, physical and temporal reasoning is given in the book by Davis (1990).”
Learning Systems of Concepts with an Infinite Relational Model (2006, background): “The systems we wish to discover are simple versions of the “domain theories” discussed by cognitive scientists and AI researchers (Davis 1990).”