508 research outputs found
Flexibly Instructable Agents
This paper presents an approach to learning from situated, interactive
tutorial instruction within an ongoing agent. Tutorial instruction is a
flexible (and thus powerful) paradigm for teaching tasks because it allows an
instructor to communicate whatever types of knowledge an agent might need in
whatever situations might arise. To support this flexibility, however, the
agent must be able to learn multiple kinds of knowledge from a broad range of
instructional interactions. Our approach, called situated explanation, achieves
such learning through a combination of analytic and inductive techniques. It
combines a form of explanation-based learning that is situated for each
instruction with a full suite of contextually guided responses to incomplete
explanations. The approach is implemented in an agent called Instructo-Soar
that learns hierarchies of new tasks and other domain knowledge from
interactive natural language instructions. Instructo-Soar meets three key
requirements of flexible instructability that distinguish it from previous
systems: (1) it can take known or unknown commands at any instruction point;
(2) it can handle instructions that apply to either its current situation or to
a hypothetical situation specified in language (as in, for instance,
conditional instructions); and (3) it can learn, from instructions, each class
of knowledge it uses to perform tasks.Comment: See http://www.jair.org/ for any accompanying file
Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations
Traditional symbolic reasoning engines, while attractive for their precision
and explicability, have a few major drawbacks: the use of brittle inference
procedures that rely on exact matching (unification) of logical terms, an
inability to deal with uncertainty, and the need for a precompiled rule-base of
knowledge (the "knowledge acquisition" problem). To address these issues, we
devise a novel logical reasoner called Braid, that supports probabilistic
rules, and uses the notion of custom unification functions and dynamic rule
generation to overcome the brittle matching and knowledge-gap problem prevalent
in traditional reasoners. In this paper, we describe the reasoning algorithms
used in Braid, and their implementation in a distributed task-based framework
that builds proof/explanation graphs for an input query. We use a simple QA
example from a children's story to motivate Braid's design and explain how the
various components work together to produce a coherent logical explanation.
Finally, we evaluate Braid on the ROC Story Cloze test and achieve close to
state-of-the-art results while providing frame-based explanations.Comment: Accepted at AAAI-202
Ontologies on the semantic web
As an informational technology, the World Wide Web has enjoyed spectacular success. In just ten years it has transformed the way information is produced, stored, and shared in arenas as diverse as shopping, family photo albums, and high-level academic research. The âSemantic Webâ was touted by its developers as equally revolutionary but has not yet achieved anything like the Webâs exponential uptake. This 17 000 word survey article explores why this might be so, from a perspective that bridges both philosophy and IT
What does semantic tiling of the cortex tell us about semantics?
Recent use of voxel-wise modeling in cognitive neuroscience suggests that semantic maps tile the cortex. Although this impressive research establishes distributed cortical areas active during the conceptual processing that underlies semantics, it tells us little about the nature of this processing. While mapping concepts between Marr's computational and implementation levels to support neural encoding and decoding, this approach ignores Marr's algorithmic level, central for understanding the mechanisms that implement cognition, in general, and conceptual processing, in particular. Following decades of research in cognitive science and neuroscience, what do we know so far about the representation and processing mechanisms that implement conceptual abilities? Most basically, much is known about the mechanisms associated with: (1) features and frame representations, (2) grounded, abstract, and linguistic representations, (3) knowledge-based inference, (4) concept composition, and (5) conceptual flexibility. Rather than explaining these fundamental representation and processing mechanisms, semantic tiles simply provide a trace of their activity over a relatively short time period within a specific learning context. Establishing the mechanisms that implement conceptual processing in the brain will require more than mapping it to cortical (and sub-cortical) activity, with process models from cognitive science likely to play central roles in specifying the intervening mechanisms. More generally, neuroscience will not achieve its basic goals until it establishes algorithmic-level mechanisms that contribute essential explanations to how the brain works, going beyond simply establishing the brain areas that respond to various task conditions
NormBank: A Knowledge Bank of Situational Social Norms
We present NormBank, a knowledge bank of 155k situational norms. This
resource is designed to ground flexible normative reasoning for interactive,
assistive, and collaborative AI systems. Unlike prior commonsense resources,
NormBank grounds each inference within a multivalent sociocultural frame, which
includes the setting (e.g., restaurant), the agents' contingent roles (waiter,
customer), their attributes (age, gender), and other physical, social, and
cultural constraints (e.g., the temperature or the country of operation). In
total, NormBank contains 63k unique constraints from a taxonomy that we
introduce and iteratively refine here. Constraints then apply in different
combinations to frame social norms. Under these manipulations, norms are
non-monotonic - one can cancel an inference by updating its frame even
slightly. Still, we find evidence that neural models can help reliably extend
the scope and coverage of NormBank. We further demonstrate the utility of this
resource with a series of transfer experiments
Artificial Cognition for Social Human-Robot Interaction: An Implementation
© 2017 The Authors HumanâRobot Interaction challenges Artificial Intelligence in many regards: dynamic, partially unknown environments that were not originally designed for robots; a broad variety of situations with rich semantics to understand and interpret; physical interactions with humans that requires fine, low-latency yet socially acceptable control strategies; natural and multi-modal communication which mandates common-sense knowledge and the representation of possibly divergent mental models. This article is an attempt to characterise these challenges and to exhibit a set of key decisional issues that need to be addressed for a cognitive robot to successfully share space and tasks with a human. We identify first the needed individual and collaborative cognitive skills: geometric reasoning and situation assessment based on perspective-taking and affordance analysis; acquisition and representation of knowledge models for multiple agents (humans and robots, with their specificities); situated, natural and multi-modal dialogue; human-aware task planning; humanârobot joint task achievement. The article discusses each of these abilities, presents working implementations, and shows how they combine in a coherent and original deliberative architecture for humanârobot interaction. Supported by experimental results, we eventually show how explicit knowledge management, both symbolic and geometric, proves to be instrumental to richer and more natural humanârobot interactions by pushing for pervasive, human-level semantics within the robot's deliberative system
Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems
Large language models (LLMs) and foundation models have been recently touted
as a game-changer for 6G systems. However, recent efforts on LLMs for wireless
networks are limited to a direct application of existing language models that
were designed for natural language processing (NLP) applications. To address
this challenge and create wireless-centric foundation models, this paper
presents a comprehensive vision on how to design universal foundation models
that are tailored towards the deployment of artificial intelligence (AI)-native
networks. Diverging from NLP-based foundation models, the proposed framework
promotes the design of large multi-modal models (LMMs) fostered by three key
capabilities: 1) processing of multi-modal sensing data, 2) grounding of
physical symbol representations in real-world wireless systems using causal
reasoning and retrieval-augmented generation (RAG), and 3) enabling
instructibility from the wireless environment feedback to facilitate dynamic
network adaptation thanks to logical and mathematical reasoning facilitated by
neuro-symbolic AI. In essence, these properties enable the proposed LMM
framework to build universal capabilities that cater to various cross-layer
networking tasks and alignment of intents across different domains. Preliminary
results from experimental evaluation demonstrate the efficacy of grounding
using RAG in LMMs, and showcase the alignment of LMMs with wireless system
designs. Furthermore, the enhanced rationale exhibited in the responses to
mathematical questions by LMMs, compared to vanilla LLMs, demonstrates the
logical and mathematical reasoning capabilities inherent in LMMs. Building on
those results, we present a sequel of open questions and challenges for LMMs.
We then conclude with a set of recommendations that ignite the path towards
LMM-empowered AI-native systems
Adding Semantic Web Knowledge to Intelligent Personal Assistant Agents
Intelligent Personal Assistant (IPA) agents are software agents which assist users in performing specific tasks. They should be able to communicate, cooperate, discuss, and guide people. This paper presentsa proposal to add Semantic Web Knowledge to IPA agents. In our solution,the IPA agent has a modular knowledge organization composed by four differentiated areas: (i) the rational area, which adds semantic webknowledge, (ii) the association area, which simplifies building appropriate responses, (iii) the commonsense area, which provides common sense responses, and (iv) the behavioral area, which allows IPA agents to show empathy. Our main objective is to create more intelligent and more humana alike IPA agents, enhancing the current abilities that these software agents provide
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