An architecture for a rational agent must allow for means-end reasoning, for the weighing of competing alternatives, and for interactions between these two forms of reasoning. Such an architecture must also address the problem of resource boundedness. We sketch a solution of the first problem that points the way to a solution of the second. In particular, we present a high-level specification of the practical-reasoning component of an architecture for a resource-bounded rational agent. In this architecture, a major role of the agent's plans is to constrain the amount of further practical reasoning she must perform. Bratman, Israel, and Pollack 3 1 Introduction Rational behavior---the production of actions that further the goals of an agent, based upon her conception of the world---has long interested researchers in artificial intelligence, who are attempting to build machines that behave rationally, as well as philosophers of mind and action, decision theorists, and others who are a...

this paper, I argue that there is an additional way in which an agent's plans can be used to constrain practical reasoning: they can suggest solutions to means-end reasoning Overloading Intentions 3 problems that the agent subsequently encounters. Moreover, such solutions can often be accepted without further deliberation about possible alternatives. An agent will often be able to guide its search for a way to achieve some goal G by looking for an action A that it already intends that can also subserve G, or, as I shall say, by looking for an intention that can be overloaded.

Practical reasoning aims at deciding what actions to perform in light of the goals a rational agent possesses. This has been a topic of interest in both philosophy and artificial intelligence, but these two disciplines have produced very different models of practical reasoning. The purpose of this paper is to examine each model in light of the other and produce a unified model adequate for the purposes of both disciplines and superior to the standard models employed by either. The philosophical (decision-theoretic) model directs activity by evaluating acts one at a time in terms of their expected utilities. It is argued that, except in certain precisely described special cases, this constitutes an inadequate theory of practical reasoning and it is shown that it leads to intuitively incorrect action prescriptions. In the general case, acts must be viewed as parts of plans, and the plans evaluated as coherent units rather than piecemeal in terms of the acts comprising them. Rationality d...

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The ability to categorize and use concepts e#ectively is a basic requirementofany intelligent actor. The utility-based approach to categorization is founded on the thesis that categorization is fundamentally in service of action, i.e., the choice of concepts made by an actor is critical to its choice of appropriate actions. This is in contrast to classical and similarity-based approaches which seek logical completeness in concept description with respect to sensory data rather than action-oriented e#ectiveness. Utility-based categorization is normative and not descriptive. It prescribes howanintelligent agent ought to conceptualize to act e#ectively. It provides ideals for categorization, speci#es criteria for the design of e#ective computational agents, and provides a model of ideal competence. A decision-theoretic framework for utilitybased categorization whichinvolves reasoning about alternative categorization models of varying levels of abstraction is proposed. Categorization mode...

Physicists have, hitherto, mostly adopted a frequentist conception of probability, according to which probability statements apply only to ensembles. It is argued that we should, instead, adopt an epistemic, or Bayesian conception, in which probabilities are conceived as logical constructs rather than physical realities, and in which probability statements do apply directly to individual events. The question is closely related to the disagreement between the orthodox school of statistical thought and the Bayesian school. It has important technical implications (it makes a difference, what statistical methodology one adopts). It may also have important implications for the interpretation of the quantum state. 1 1.

Einstein initially objected to the probabilistic aspect of quantum mechanics— the idea that God is playing at dice. Later he changed his ground, and focussed instead on the point that the Copenhagen Interpretation leads to what Einstein saw as the abandonment of physical realism. We argue here that Einstein’s initial intuition was perfectly sound, and that it is precisely the fact that quantum mechanics is a fundamentally probabilistic theory which is at the root of all the controversies regarding its interpretation. Probability is an intrinsically logical concept. This means that the quantum state has an essentially logical significance. It is extremely difficult to reconcile that fact with Einstein’s belief, that it is the task of physics to give us a vision of the world apprehended sub specie aeternitatis. Quantum mechanics thus presents us with a simple choice: either to follow Einstein in looking for a theory which is not probabilistic at the fundamental level, or else to accept that physics does not in fact put us in the position of God looking down on things from above. There is a widespread fear that the latter alternative must inevitably lead to a greatly impoverished,

This paper reviews and formalizes algorithms for probabilistic inferences upon causal probabilistic networks (CPN), also known as Bayesian networks, and introduces Probanet - a development environment for CPNs. Information fusion in CPNs is realized through updating joint probabilities of the variables upon the arrival of new evidences or new hypotheses. Kernel algorithms for some dominant methods of inferences are formalized from discontiguous, mathematics-oriented literatures, with gaps filled in with regards to computability and completeness. Probanet has been designed and developed as a generic shell, a development environment for CPN construction and application. The design aspects and current status of Probanet are described. 1 Introduction Digital signal processing has entered the era of multisensor data fusion and multisource information fusion. Whatever the application may be, the process of data and information fusion generally involves multiple data types such as sensor sig...

Quantum mechanics is a fundamentally probabilistic theory (at least so far as the empirical predictions are concerned). It follows that, if one wants to properly understand quantum mechanics, it is essential to clearly understand the meaning of probability statements. The interpretation of probability has excited nearly as much philosophical controversy as the interpretation of quantum mechanics. 20 th century physicists have mostly adopted a frequentist conception. In this paper it is argued that we ought, instead, to adopt a logical or Bayesian conception. The paper includes a comparison of the orthodox and Bayesian theories of statistical inference. It concludes with a few remarks concerning the implications for the concept of physical reality. 1 1.

Abstract. “Least regret control ” consists in trying to find a control which “optimizes the situation” with the constraint of not making things too worse with respect to a known reference control, in presence of more or less significant perturbations. This notion was introduced in [7]. It is recalled on a simple example (an elliptic system, with distributed control and boundary perturbation) in Section 2. We show that the problem reduces to a standard optimal control problem for augmented state equations. On another hand, we have introduced in recent notes [9–12] the method of virtual control, aimed at the “decomposition of everything ” (decomposition of the domain, of the operator, etc). An introduction to this method is presented, without a priori knowledge needed, in Sections 3 and 4, directly on the augmented state equations. For problems without control, or with “standard ” control, numerical applications of the virtual control ideas have been given in the notes [9–12] and in the note [5]. One of the first systematic paper devoted to all kind of decomposition methods, including multicriteria, is a joint paper with A. Bensoussan and R. Temam, to whom this paper is dedicated, cf. [1].