40,847 research outputs found

    Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems

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    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

    Intelligent control based on fuzzy logic and neural net theory

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    In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment

    Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

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    Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

    Modelling default and likelihood reasoning as probabilistic

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    A probabilistic analysis of plausible reasoning about defaults and about likelihood is presented. 'Likely' and 'by default' are in fact treated as duals in the same sense as 'possibility' and 'necessity'. To model these four forms probabilistically, a logic QDP and its quantitative counterpart DP are derived that allow qualitative and corresponding quantitative reasoning. Consistency and consequence results for subsets of the logics are given that require at most a quadratic number of satisfiability tests in the underlying propositional logic. The quantitative logic shows how to track the propagation error inherent in these reasoning forms. The methodology and sound framework of the system highlights their approximate nature, the dualities, and the need for complementary reasoning about relevance

    Puzzles of Anthropic Reasoning Resolved Using Full Non-indexical Conditioning

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    I consider the puzzles arising from four interrelated problems involving `anthropic' reasoning, and in particular the `Self-Sampling Assumption' (SSA) - that one should reason as if one were randomly chosen from the set of all observers in a suitable reference class. The problem of Freak Observers might appear to force acceptance of SSA if any empirical evidence is to be credited. The Sleeping Beauty problem arguably shows that one should also accept the `Self-Indication Assumption' (SIA) - that one should take one's own existence as evidence that the number of observers is more likely to be large than small. But this assumption produces apparently absurd results in the Presumptuous Philosopher problem. Without SIA, however, a definitive refutation of the counterintuitive Doomsday Argument seems difficult. I show that these problems are satisfyingly resolved by applying the principle that one should always condition on all evidence - not just on the fact that you are an intelligent observer, or that you are human, but on the fact that you are a human with a specific set of memories. This `Full Non-indexical Conditioning' (FNC) approach usually produces the same results as assuming both SSA and SIA, with a sufficiently broad reference class, while avoiding their ad hoc aspects. I argue that the results of FNC are correct using the device of hypothetical ``companion'' observers, whose existence clarifies what principles of reasoning are valid. I conclude by discussing how one can use FNC to infer how densely we should expect intelligent species to occur, and by examining recent anthropic arguments in inflationary and string theory cosmology

    Automatic goal allocation for a planetary rover with DSmT

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    In this chapter, we propose an approach for assigning aninterest level to the goals of a planetary rover. Assigning an interest level to goals, allows the rover to autonomously transform and reallocate the goals. The interest level is defined by data-fusing payload and navigation information. The fusion yields an 'interest map',that quantifies the level of interest of each area around the rover. In this way the planner can choose the most interesting scientific objectives to be analysed, with limited human intervention, and reallocates its goals autonomously. The Dezert-Smarandache Theory of Plausible and Paradoxical Reasoning was used for information fusion: this theory allows dealing with vague and conflicting data. In particular, it allows us to directly model the behaviour of the scientists that have to evaluate the relevance of a particular set of goals. This chaptershows an application of the proposed approach to the generation of a reliable interest map
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