368 research outputs found
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought
How does language inform our downstream thinking? In particular, how do
humans make meaning from language -- and how can we leverage a theory of
linguistic meaning to build machines that think in more human-like ways? In
this paper, we propose \textit{rational meaning construction}, a computational
framework for language-informed thinking that combines neural models of
language with probabilistic models for rational inference. We frame linguistic
meaning as a context-sensitive mapping from natural language into a
\textit{probabilistic language of thought} (PLoT) -- a general-purpose symbolic
substrate for probabilistic, generative world modeling. Our architecture
integrates two powerful computational tools that have not previously come
together: we model thinking with \textit{probabilistic programs}, an expressive
representation for flexible commonsense reasoning; and we model meaning
construction with \textit{large language models} (LLMs), which support
broad-coverage translation from natural language utterances to code expressions
in a probabilistic programming language. We illustrate our framework in action
through examples covering four core domains from cognitive science:
probabilistic reasoning, logical and relational reasoning, visual and physical
reasoning, and social reasoning about agents and their plans. In each, we show
that LLMs can generate context-sensitive translations that capture
pragmatically-appropriate linguistic meanings, while Bayesian inference with
the generated programs supports coherent and robust commonsense reasoning. We
extend our framework to integrate cognitively-motivated symbolic modules to
provide a unified commonsense thinking interface from language. Finally, we
explore how language can drive the construction of world models themselves
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A neural-symbolic system for temporal reasoning with application to model verification and learning
The effective integration of knowledge representation, reasoning and learning into a robust computational model is one of the key challenges in Computer Science and Artificial Intelligence. In particular, temporal models have been fundamental in describing the behaviour of Computational and Neural-Symbolic Systems. Furthermore, knowledge acquisition of correct descriptions of the desired systemâs behaviour is a complex task in several domains. Several efforts have been directed towards the development of tools that are capable of learning, describing and evolving software models.
This thesis contributes to two major areas of Computer Science, namely Artificial Intelligence (AI) and Software Engineering. Under an AI perspective, we present a novel neural-symbolic computational model capable of representing and learning temporal knowledge in recurrent networks. The model works in integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models to a set of desirable system properties and effective learning from examples, which in turn can lead to symbolic temporal knowledge extraction from the corresponding trained neural networks. The model is sound, from a theoretical standpoint, but is also tested in a number of case studies.
An extension to the framework is shown to tackle aspects of verification and adaptation under the SE perspective. As regards verification, we make use of established techniques for model checking, which allow the verification of properties described as temporal models and return counter-examples whenever the properties are not satisfied. Our neural-symbolic framework is then extended to deal with different sources of information. This includes the translation of model descriptions into the neural structure, the evolution of such descriptions by the application of learning of counter examples, and also the learning of new models from simple observation of their behaviour.
In summary, we believe the thesis describes a principled methodology for temporal knowledge representation, learning and extraction, shedding new light on predictive temporal models, not only from a theoretical standpoint, but also with respect to a potentially large number of applications in AI, Neural Computation and Software Engineering, where temporal knowledge plays a fundamental role
Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning
The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques
Second CLIPS Conference Proceedings, volume 1
Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems
Fourth Conference on Artificial Intelligence for Space Applications
Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming
Representing intelligent decision making in discrete event simulation : a stochastic neural network approach
The problem of representing decision making behaviour in discrete event simulation was
investigated. Of particular interest was modelling variety in the decisions, where
different people might make different decisions even where the same circumstances hold.
An initial investigation of existing and alternative approaches for representing decision
making was carried out. This led to the suggestion of using a neural network to
represent the decision making behaviour in the form of a multi-criteria probability
distribution based on data of observed decision making.
The feasibility of the stochastic neural network approach was investigated. Models were
fitted using artificial data from discrete and continuous distributions that included the
shape parameters as inputs, and tested against known results from the distributions. Also
a bank simulation was used to collect data from volunteers who controlled the queuing
decisions of customers inside the bank. Models of their behaviour were created and
implemented in the bank simulation to automate the decision making of customers.
The investigation established the feasibility of the approach, although it indicated the
need for substantial amounts of data showing examples of decision making. A hybrid
model that combined the stochastic neural network approach with a rule-based approach
allowed the development of more general models of decision making behaviour
Player agency in interactive narrative: audience, actor & author
The question motivating this review paper is, how can
computer-based interactive narrative be used as a constructivist learn-
ing activity? The paper proposes that player agency can be used to
link interactive narrative to learner agency in constructivist theory,
and to classify approaches to interactive narrative. The traditional
question driving research in interactive narrative is, âhow can an in-
teractive narrative deal with a high degree of player agency, while
maintaining a coherent and well-formed narrative?â This question
derives from an Aristotelian approach to interactive narrative that,
as the question shows, is inherently antagonistic to player agency.
Within this approach, player agency must be restricted and manip-
ulated to maintain the narrative. Two alternative approaches based
on Brechtâs Epic Theatre and Boalâs Theatre of the Oppressed are
reviewed. If a Boalian approach to interactive narrative is taken the
conflict between narrative and player agency dissolves. The question
that emerges from this approach is quite different from the traditional
question above, and presents a more useful approach to applying in-
teractive narrative as a constructivist learning activity
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