178 research outputs found
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
An Application of Declarative Languages in Distributed Architectures: ASP and DALI Microservices
In this paper we introduce an approach to the possible adoption of Answer Set Programming (ASP) for the definition of microservices, which are a successful abstraction for designing distributed applications as suites of independently deployable interacting components. Such ASP-based components might be employed in distributed architectures related to Cloud Computing or to the Internet of Things (IoT), where the ASP microservices might be usefully coordinated with intelligent logic-based agents. We develop a case study where we consider ASP microservices in synergy with agents defined in DALI, a well-known logic-based agent-oriented programming language developed by our research group
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A general approach to temporal reasoning about action and change
Reasoning about actions and change based on common sense knowledge is one of the most important and difficult tasks in the artificial intelligence research area. A series of such tasks are identified which motivate the consideration and application of reasoning formalisms. There follows a discussion of the broad issues involved in modelling time and constructing a logical language. In general, worlds change over time. To model the dynamic world, the ability to predict what the state of the world will be after the execution of a particular sequence of actions, which take time and to explain how some given state change came about, i.e. the causality are basic requirements of any autonomous rational agent.
The research work presented herein addresses some of the fundamental concepts and the relative issues in formal reasoning about actions and change. In this thesis, we employ a new time structure, which helps to deal with the so-called intermingling problem and the dividing instant problem. Also, the issue of how to treat the relationship between a time duration and its relative time entity is examined. In addition, some key terms for representing and reasoning about actions and change, such as states, situations, actions and events are formulated. Furthermore, a new formalism for reasoning about change over time is presented. It allows more flexible temporal causal relationships than do other formalisms for reasoning about causal change, such as the situation calculus and the event calculus. It includes effects that start during, immediately after, or some time after their causes, and which end before, simultaneously with, or after their causes. The presented formalism allows the expression of common-sense causal laws at high level. Also, it is shown how these laws can be used to deduce state change over time at low level. Finally, we show that the approach provided here is expressive
Propagators and Solvers for the Algebra of Modular Systems
To appear in the proceedings of LPAR 21.
Solving complex problems can involve non-trivial combinations of distinct
knowledge bases and problem solvers. The Algebra of Modular Systems is a
knowledge representation framework that provides a method for formally
specifying such systems in purely semantic terms. Formally, an expression of
the algebra defines a class of structures. Many expressive formalism used in
practice solve the model expansion task, where a structure is given on the
input and an expansion of this structure in the defined class of structures is
searched (this practice overcomes the common undecidability problem for
expressive logics). In this paper, we construct a solver for the model
expansion task for a complex modular systems from an expression in the algebra
and black-box propagators or solvers for the primitive modules. To this end, we
define a general notion of propagators equipped with an explanation mechanism,
an extension of the alge- bra to propagators, and a lazy conflict-driven
learning algorithm. The result is a framework for seamlessly combining solving
technology from different domains to produce a solver for a combined system.Comment: To appear in the proceedings of LPAR 2
Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review
Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that
combining deep learning with symbolic reasoning will lead to stronger AI than
either paradigm on its own. As successful as deep learning has been, it is
generally accepted that even our best deep learning systems are not very good
at abstract reasoning. And since reasoning is inextricably linked to language,
it makes intuitive sense that Natural Language Processing (NLP), would be a
particularly well-suited candidate for NeSy. We conduct a structured review of
studies implementing NeSy for NLP, with the aim of answering the question of
whether NeSy is indeed meeting its promises: reasoning, out-of-distribution
generalization, interpretability, learning and reasoning from small data, and
transferability to new domains. We examine the impact of knowledge
representation, such as rules and semantic networks, language structure and
relational structure, and whether implicit or explicit reasoning contributes to
higher promise scores. We find that systems where logic is compiled into the
neural network lead to the most NeSy goals being satisfied, while other factors
such as knowledge representation, or type of neural architecture do not exhibit
a clear correlation with goals being met. We find many discrepancies in how
reasoning is defined, specifically in relation to human level reasoning, which
impact decisions about model architectures and drive conclusions which are not
always consistent across studies. Hence we advocate for a more methodical
approach to the application of theories of human reasoning as well as the
development of appropriate benchmarks, which we hope can lead to a better
understanding of progress in the field. We make our data and code available on
github for further analysis.Comment: Surve
Modelling causal reasoning
PhDAlthough human causal reasoning is widely acknowledged as an object
of scientific enquiry, there is little consensus on an appropriate measure
of progress. Up-to-date evidence of the standard method of research in
the field shows that this method has been rejected at the birth of modern
science.
We describe an instance of the standard scientific method for modelling
causal reasoning (causal calculators). The method allows for uniform
proofs of three relevant computational properties: correctness of the model
with respect to the intended model, full abstraction of the model (function)
with respect to the equivalence of reasoning scenarios (input), and formal
relations of equivalence and subsumption between models. The method
extends and exploits the systematic paradigm [Handbook of Logic in Artificial
Intelligence and Logic Programming, volume IV, p. 439-498, Oxford 1995] to
fit with our interpretation of it.
Using the described method, we present results for some major models,
with an updated summary spanning seventy-two years of research in the
field
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