18,932 research outputs found
Recognition of Mental Adjectives in An Efficient and Automatic Style
In recent years, commonsense reasoning has received more and more attention
from academic community. We propose a new lexical inference task, Mental and
Physical Classification (MPC), to handle commonsense reasoning in a reasoning
graph. Mental words relate to mental activities, which fall into six
categories: Emotion, Need, Perceiving, Reasoning, Planning and Personality.
Physical words describe physical attributes of an object, like color, hardness,
speed and malleability. A BERT model is fine-tuned for this task and active
learning algorithm is adopted in the training framework to reduce the required
annotation resources. The model using ENTROPY strategy achieves satisfactory
accuracy and requires only about 300 labeled words. We also compare our result
with SentiWordNet to check the difference between MPC and subjectivity
classification task in sentiment analysis.Comment: 10 pages, 2 figure
Narrative based Postdictive Reasoning for Cognitive Robotics
Making sense of incomplete and conflicting narrative knowledge in the
presence of abnormalities, unobservable processes, and other real world
considerations is a challenge and crucial requirement for cognitive robotics
systems. An added challenge, even when suitably specialised action languages
and reasoning systems exist, is practical integration and application within
large-scale robot control frameworks.
In the backdrop of an autonomous wheelchair robot control task, we report on
application-driven work to realise postdiction triggered abnormality detection
and re-planning for real-time robot control: (a) Narrative-based knowledge
about the environment is obtained via a larger smart environment framework; and
(b) abnormalities are postdicted from stable-models of an answer-set program
corresponding to the robot's epistemic model. The overall reasoning is
performed in the context of an approximate epistemic action theory based
planner implemented via a translation to answer-set programming.Comment: Commonsense Reasoning Symposium, Ayia Napa, Cyprus, 201
Story Ending Generation with Incremental Encoding and Commonsense Knowledge
Generating a reasonable ending for a given story context, i.e., story ending
generation, is a strong indication of story comprehension. This task requires
not only to understand the context clues which play an important role in
planning the plot but also to handle implicit knowledge to make a reasonable,
coherent story.
In this paper, we devise a novel model for story ending generation. The model
adopts an incremental encoding scheme to represent context clues which are
spanning in the story context. In addition, commonsense knowledge is applied
through multi-source attention to facilitate story comprehension, and thus to
help generate coherent and reasonable endings. Through building context clues
and using implicit knowledge, the model is able to produce reasonable story
endings. context clues implied in the post and make the inference based on it.
Automatic and manual evaluation shows that our model can generate more
reasonable story endings than state-of-the-art baselines.Comment: Accepted in AAAI201
Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems
In this paper I will present an analysis of the impact that the notion of “bounded rationality”,
introduced by Herbert Simon in his book “Administrative Behavior”, produced in the
field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated
Decision Making (ADM), I will show how the introduction of the cognitive dimension into
the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the
development of a line of research aiming at the realisation of artificial systems whose decisions
are based on the adoption of powerful shortcut strategies (known as heuristics) based
on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the
“heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial
complexity in real-life situations and still represents an important strategy for the design
and implementation of intelligent systems
Between Sense and Sensibility: Declarative narrativisation of mental models as a basis and benchmark for visuo-spatial cognition and computation focussed collaborative cognitive systems
What lies between `\emph{sensing}' and `\emph{sensibility}'? In other words,
what kind of cognitive processes mediate sensing capability, and the formation
of sensible impressions ---e.g., abstractions, analogies, hypotheses and theory
formation, beliefs and their revision, argument formation--- in domain-specific
problem solving, or in regular activities of everyday living, working and
simply going around in the environment? How can knowledge and reasoning about
such capabilities, as exhibited by humans in particular problem contexts, be
used as a model and benchmark for the development of collaborative cognitive
(interaction) systems concerned with human assistance, assurance, and
empowerment?
We pose these questions in the context of a range of assistive technologies
concerned with \emph{visuo-spatial perception and cognition} tasks encompassing
aspects such as commonsense, creativity, and the application of specialist
domain knowledge and problem-solving thought processes. Assistive technologies
being considered include: (a) human activity interpretation; (b) high-level
cognitive rovotics; (c) people-centred creative design in domains such as
architecture & digital media creation, and (d) qualitative analyses geographic
information systems. Computational narratives not only provide a rich cognitive
basis, but they also serve as a benchmark of functional performance in our
development of computational cognitive assistance systems. We posit that
computational narrativisation pertaining to space, actions, and change provides
a useful model of \emph{visual} and \emph{spatio-temporal thinking} within a
wide-range of problem-solving tasks and application areas where collaborative
cognitive systems could serve an assistive and empowering function.Comment: 5 pages, research statement summarising recent publication
Learning and Reasoning for Robot Sequential Decision Making under Uncertainty
Robots frequently face complex tasks that require more than one action, where
sequential decision-making (SDM) capabilities become necessary. The key
contribution of this work is a robot SDM framework, called LCORPP, that
supports the simultaneous capabilities of supervised learning for passive state
estimation, automated reasoning with declarative human knowledge, and planning
under uncertainty toward achieving long-term goals. In particular, we use a
hybrid reasoning paradigm to refine the state estimator, and provide
informative priors for the probabilistic planner. In experiments, a mobile
robot is tasked with estimating human intentions using their motion
trajectories, declarative contextual knowledge, and human-robot interaction
(dialog-based and motion-based). Results suggest that, in efficiency and
accuracy, our framework performs better than its no-learning and no-reasoning
counterparts in office environment.Comment: In proceedings of 34th AAAI conference on Artificial Intelligence,
202
ROTUNDE - A Smart Meeting Cinematography Initiative: Tools, Datasets, and Benchmarks for Cognitive Interpretation and Control
We construe smart meeting cinematography with a focus on professional
situations such as meetings and seminars, possibly conducted in a distributed
manner across socio-spatially separated groups. The basic objective in smart
meeting cinematography is to interpret professional interactions involving
people, and automatically produce dynamic recordings of discussions, debates,
presentations etc in the presence of multiple communication modalities. Typical
modalities include gestures (e.g., raising one's hand for a question,
applause), voice and interruption, electronic apparatus (e.g., pressing a
button), movement (e.g., standing-up, moving around) etc. ROTUNDE, an instance
of smart meeting cinematography concept, aims to: (a) develop
functionality-driven benchmarks with respect to the interpretation and control
capabilities of human-cinematographers, real-time video editors, surveillance
personnel, and typical human performance in everyday situations; (b) Develop
general tools for the commonsense cognitive interpretation of dynamic scenes
from the viewpoint of visuo-spatial cognition centred perceptual
narrativisation. Particular emphasis is placed on declarative representations
and interfacing mechanisms that seamlessly integrate within large-scale
cognitive (interaction) systems and companion technologies consisting of
diverse AI sub-components. For instance, the envisaged tools would provide
general capabilities for high-level commonsense reasoning about space, events,
actions, change, and interaction.Comment: Appears in AAAI-2013 Workshop on: Space, Time, and Ambient
Intelligence (STAMI 2013
Physical Reasoning and Object Planning for Household Embodied Agents
In this study, we explore the sophisticated domain of task planning for
robust household embodied agents, with a particular emphasis on the intricate
task of selecting substitute objects. We introduce the CommonSense Object
Affordance Task (COAT), a novel framework designed to analyze reasoning
capabilities in commonsense scenarios. This approach is centered on
understanding how these agents can effectively identify and utilize alternative
objects when executing household tasks, thereby offering insights into the
complexities of practical decision-making in real-world environments.Drawing
inspiration from human decision-making, we explore how large language models
tackle this challenge through three meticulously crafted commonsense
question-and-answer datasets, featuring refined rules and human annotations.
Our evaluation of state-of-the-art language models on these datasets sheds
light on three pivotal considerations: 1) aligning an object's inherent utility
with the task at hand, 2) navigating contextual dependencies (societal norms,
safety, appropriateness, and efficiency), and 3) accounting for the current
physical state of the object. To maintain accessibility, we introduce five
abstract variables reflecting an object's physical condition, modulated by
human insights to simulate diverse household scenarios. Our contributions
include insightful Object-Utility mappings addressing the first consideration
and two extensive QA datasets (15k and 130k questions) probing the intricacies
of contextual dependencies and object states. The datasets, along with our
findings, are accessible at: \url{https://github.com/com-phy-affordance/COAT}.
This research not only advances our understanding of physical commonsense
reasoning in language models but also paves the way for future improvements in
household agent intelligence.Comment: Total: 32 pages ( 16 pages main content, 11 Figures
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