A Defeasible Calculus for Zetetic Agents
- URL: http://arxiv.org/abs/2010.05293v1
- Date: Sun, 11 Oct 2020 17:39:03 GMT
- Title: A Defeasible Calculus for Zetetic Agents
- Authors: Jared Millson
- Abstract summary: We show that zetetic norms can be modeled via defeasible inferences to and from questions.
We offer a sequent calculus that accommodates unique features of "erotetic defeat"
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The study of defeasible reasoning unites epistemologists with those working
in AI, in part, because both are interested in epistemic rationality. While it
is traditionally thought to govern the formation and (with)holding of beliefs,
epistemic rationality may also apply to the interrogative attitudes associated
with our core epistemic practice of inquiry, such as wondering, investigating,
and curiosity. Since generally intelligent systems should be capable of
rational inquiry, AI researchers have a natural interest in the norms that
govern interrogative attitudes. Following its recent coinage, we use the term
"zetetic" to refer to the properties and norms associated with the capacity to
inquire. In this paper, we argue that zetetic norms can be modeled via
defeasible inferences to and from questions---a.k.a erotetic inferences---in a
manner similar to the way norms of epistemic rationality are represented by
defeasible inference rules. We offer a sequent calculus that accommodates the
unique features of "erotetic defeat" and that exhibits the computational
properties needed to inform the design of zetetic agents. The calculus
presented here is an improved version of the one presented in Millson (2019),
extended to cover a new class of defeasible erotetic inferences.
Related papers
- Are language models rational? The case of coherence norms and belief revision [63.78798769882708]
We consider logical coherence norms as well as coherence norms tied to the strength of belief in language models.
We argue that rational norms tied to coherence do apply to some language models, but not to others.
arXiv Detail & Related papers (2024-06-05T16:36:21Z) - Betting on what is neither verifiable nor falsifiable [18.688474183114085]
We propose an approach to betting on such events via options, or equivalently as bets on the outcome of a "verification-falsification game"
Our work thus acts as an alternative to the existing framework of Garrabrant induction for logical uncertainty, and relates to the stance known as constructivism in the philosophy of mathematics.
arXiv Detail & Related papers (2024-01-29T17:30:34Z) - Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement [92.61557711360652]
Language models (LMs) often fall short on inductive reasoning, despite achieving impressive success on research benchmarks.
We conduct a systematic study of the inductive reasoning capabilities of LMs through iterative hypothesis refinement.
We reveal several discrepancies between the inductive reasoning processes of LMs and humans, shedding light on both the potentials and limitations of using LMs in inductive reasoning tasks.
arXiv Detail & Related papers (2023-10-12T17:51:10Z) - Crystal: Introspective Reasoners Reinforced with Self-Feedback [118.53428015478957]
We propose a novel method to develop an introspective commonsense reasoner, Crystal.
To tackle commonsense problems, it first introspects for knowledge statements related to the given question, and subsequently makes an informed prediction that is grounded in the previously introspected knowledge.
Experiments show that Crystal significantly outperforms both the standard supervised finetuning and chain-of-thought distilled methods, and enhances the transparency of the commonsense reasoning process.
arXiv Detail & Related papers (2023-10-07T21:23:58Z) - A Semantic Approach to Decidability in Epistemic Planning (Extended
Version) [72.77805489645604]
We use a novel semantic approach to achieve decidability.
Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity.
We prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest.
arXiv Detail & Related papers (2023-07-28T11:26:26Z) - Learnability with PAC Semantics for Multi-agent Beliefs [38.88111785113001]
The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence.
Valiant recognised that the challenge of learning should be integrated with deduction.
Although weaker than classical entailment, it allows for a powerful model-theoretic framework for answering queries.
arXiv Detail & Related papers (2023-06-08T18:22:46Z) - Observing Interventions: A logic for thinking about experiments [62.997667081978825]
This paper makes a first step towards a logic of learning from experiments.
Crucial for our approach is the idea that the notion of an intervention can be used as a formal expression of a (real or hypothetical) experiment.
For all the proposed logical systems, we provide a sound and complete axiomatization.
arXiv Detail & Related papers (2021-11-25T09:26:45Z) - A Description Logic for Analogical Reasoning [28.259681405091666]
We present a mechanism to infer plausible missing knowledge, which relies on reasoning by analogy.
This is the first paper that studies analog reasoning within the setting of description logic.
arXiv Detail & Related papers (2021-05-10T19:06:07Z) - Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and
Execution [97.50813120600026]
Spatial-temporal reasoning is a challenging task in Artificial Intelligence (AI)
Recent works have focused on an abstract reasoning task of this kind -- Raven's Progressive Matrices ( RPM)
We propose a neuro-symbolic Probabilistic Abduction and Execution learner (PrAE) learner.
arXiv Detail & Related papers (2021-03-26T02:42:18Z) - Logic, Probability and Action: A Situation Calculus Perspective [12.47276164048813]
The unification of logic and probability is a long-standing concern in AI.
We explore recent results pertaining to the integration of logic, probability and actions in the situation calculus.
Results are motivated in the context of cognitive robotics.
arXiv Detail & Related papers (2020-06-17T13:49:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.