1,527 research outputs found
Neural Belief Reasoner
This paper proposes a new generative model called neural belief reasoner
(NBR). It differs from previous models in that it specifies a belief function
rather than a probability distribution. Its implementation consists of neural
networks, fuzzy-set operations and belief-function operations, and
query-answering, sample-generation and training algorithms are presented. This
paper studies NBR in two tasks. The first is a synthetic unsupervised-learning
task, which demonstrates NBR's ability to perform multi-hop reasoning,
reasoning with uncertainty and reasoning about conflicting information. The
second is supervised learning: a robust MNIST classifier for 4 and 9, which is
the most challenging pair of digits. This classifier needs no adversarial
training, and it substantially exceeds the state of the art in adversarial
robustness as measured by the L2 metric, while at the same time maintains 99.1%
accuracy on natural images
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
Ideal Reasoners donât Believe in Zombies
The negative zombie argument concludes that physicalism is false from the premises that p â§ÂŹq is ideally negatively conceivable and that what is ideally negatively conceivable is possible, where p is the conjunction of the fundamental physical truths and laws and q is a phenomenal truth (Chalmers 2002; 2010). A sentence Ï is ideally negatively conceivable iff Ï is not ruled out a priori on ideal rational reflection. In this paper, I argue that the negative zombie argument is neither a priori nor conclusive. First, I argue that the premises of the argument are true only if there exists an adequate finite ideal reasoner R that believes â(p ⧠q) on the basis of not believing pâq on a priori basis. Roughly, a finite reasoner is a reasoner with cognitive limitations (e.g. finite memory). I argue that R is finite only if R reasons nonmonotonically and only approach ideal reflection at the limit of a reasoning sequence. This would render the argument nonconclusive. Finally, I argue that, for some q, R does not believe â(p ⧠q) on the basis of not believing pâq on a priori basis (e.g. for q =âsomething is consciousâ). This would render the choice of an adequate q dependent on empirical information (and the argument a posteriori). I conclude that the negative zombie argument (and, maybe, all zombie arguments) is neither a priori nor conclusive
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
We propose a set of compositional design patterns to describe a large variety
of systems that combine statistical techniques from machine learning with
symbolic techniques from knowledge representation. As in other areas of
computer science (knowledge engineering, software engineering, ontology
engineering, process mining and others), such design patterns help to
systematize the literature, clarify which combinations of techniques serve
which purposes, and encourage re-use of software components. We have validated
our set of compositional design patterns against a large body of recent
literature.Comment: 12 pages,55 reference
Unconscious Inference Theories of Cognitive Acheivement
This chapter argues that the only tenable unconscious inferences theories of cognitive achievement are ones that employ a theory internal technical notion of representation, but that once we give cash-value definitions of the relevant notions of representation and inference, there is little left of the ordinary notion of representation. We suggest that the real value of talk of unconscious inferences lies in (a) their heuristic utility in helping us to make fruitful predictions, such as about illusions, and (b) their providing a high-level description of the functional organization of subpersonal faculties that makes clear how they equip an agent to navigate its environment and pursue its goals
Toward machines that behave ethically better than humans do
With the increasing dependence on autonomous operating agents and robots the need for ethical machine behavior rises. This paper presents a moral reasoner that combines connectionism, utilitarianism and ethical theory about moral duties. The moral decision-making matches the analysis of expert ethicists in the health domain. This may be useful in many applications, especially where machines interact with humans in a medical context. Additionally, when connected to a cognitive model of emotional intelligence and affective decision making, it can be explored how moral decision making impacts affective behavior
The intersection between Descriptivism and Meliorism in reasoning research: further proposals in support of 'soft normativism'
The rationality paradox centres on the observation that people are highly intelligent, yet show evidence of errors and biases in their thinking when measured against normative standards. Elqayam and Evans (e.g., 2011) reject normative standards in the psychological study of thinking, reasoning and deciding in favour of a âvalue-freeâ descriptive approach to studying high-level cognition. In reviewing Elqayam and Evansâ position, we defend an alternative to descriptivism in the form of âsoft normativismâ, which allows for normative evaluations alongside the pursuit of descriptive research goals. We propose that normative theories have considerable value provided that researchers: (1) are alert to the philosophical quagmire of strong relativism; (2) are mindful of the biases that can arise from utilising normative benchmarks; and (3) engage in a focused analysis of the processing approach adopted by individual reasoners. We address the controversial âisâoughtâ inference in this context and appeal to a âbridging solutionâ to this contested inference that is based on the concept of âinformal reflective equilibriumâ. Furthermore, we draw on Elqayam and Evansâ recognition of a role for normative benchmarks in research programmes that are devised to enhance reasoning performance and we argue that such Meliorist research programmes have a valuable reciprocal relationship with descriptivist accounts of reasoning. In sum, we believe that descriptions of reasoning processes are fundamentally enriched by evaluations of reasoning quality, and argue that if such standards are discarded altogether then our explanations and descriptions of reasoning processes are severely undermined
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