100,251 research outputs found
Reasoning About the Transfer of Control
We present DCL-PC: a logic for reasoning about how the abilities of agents
and coalitions of agents are altered by transferring control from one agent to
another. The logical foundation of DCL-PC is CL-PC, a logic for reasoning about
cooperation in which the abilities of agents and coalitions of agents stem from
a distribution of atomic Boolean variables to individual agents -- the choices
available to a coalition correspond to assignments to the variables the
coalition controls. The basic modal constructs of DCL-PC are of the form
coalition C can cooperate to bring about phi. DCL-PC extends CL-PC with dynamic
logic modalities in which atomic programs are of the form agent i gives control
of variable p to agent j; as usual in dynamic logic, these atomic programs may
be combined using sequence, iteration, choice, and test operators to form
complex programs. By combining such dynamic transfer programs with cooperation
modalities, it becomes possible to reason about how the power of agents and
coalitions is affected by the transfer of control. We give two alternative
semantics for the logic: a direct semantics, in which we capture the
distributions of Boolean variables to agents; and a more conventional Kripke
semantics. We prove that these semantics are equivalent, and then present an
axiomatization for the logic. We investigate the computational complexity of
model checking and satisfiability for DCL-PC, and show that both problems are
PSPACE-complete (and hence no worse than the underlying logic CL-PC). Finally,
we investigate the characterisation of control in DCL-PC. We distinguish
between first-order control -- the ability of an agent or coalition to control
some state of affairs through the assignment of values to the variables under
the control of the agent or coalition -- and second-order control -- the
ability of an agent to exert control over the control that other agents have by
transferring variables to other agents. We give a logical characterisation of
second-order control
Action Logic Programs: How to Specify Strategic Behavior in Dynamic Domains Using Logical Rules
We discuss a new concept of agent programs that combines logic programming with reasoning about actions. These agent logic programs are characterized by a clear separation between the specification of the agent’s strategic behavior and the underlying theory about the agent’s actions and their effects. This makes it a generic, declarative agent programming language, which can be combined with an action representation formalism of one’s choice. We present a declarative semantics for agent logic programs along with (two versions of) a sound and complete operational semantics, which combines the standard inference mechanisms for (constraint) logic programs with reasoning about actions
Verification of Agent-Based Artifact Systems
Artifact systems are a novel paradigm for specifying and implementing
business processes described in terms of interacting modules called artifacts.
Artifacts consist of data and lifecycles, accounting respectively for the
relational structure of the artifacts' states and their possible evolutions
over time. In this paper we put forward artifact-centric multi-agent systems, a
novel formalisation of artifact systems in the context of multi-agent systems
operating on them. Differently from the usual process-based models of services,
the semantics we give explicitly accounts for the data structures on which
artifact systems are defined. We study the model checking problem for
artifact-centric multi-agent systems against specifications written in a
quantified version of temporal-epistemic logic expressing the knowledge of the
agents in the exchange. We begin by noting that the problem is undecidable in
general. We then identify two noteworthy restrictions, one syntactical and one
semantical, that enable us to find bisimilar finite abstractions and therefore
reduce the model checking problem to the instance on finite models. Under these
assumptions we show that the model checking problem for these systems is
EXPSPACE-complete. We then introduce artifact-centric programs, compact and
declarative representations of the programs governing both the artifact system
and the agents. We show that, while these in principle generate infinite-state
systems, under natural conditions their verification problem can be solved on
finite abstractions that can be effectively computed from the programs. Finally
we exemplify the theoretical results of the paper through a mainstream
procurement scenario from the artifact systems literature
The Logic of the Method of Agent-Based Simulation in the Social Sciences: Empirical and Intentional Adequacy of Computer Programs
The classical theory of computation does not represent an adequate model of reality for simulation in the social sciences. The aim of this paper is to construct a methodological perspective that is able to conciliate the formal and empirical logic of program verification in computer science, with the interpretative and multiparadigmatic logic of the social sciences. We attempt to evaluate whether social simulation implies an additional perspective about the way one can understand the concepts of program and computation. We demonstrate that the logic of social simulation implies at least two distinct types of program verifications that reflect an epistemological distinction in the kind of knowledge one can have about programs. Computer programs seem to possess a causal capability (Fetzer, 1999) and an intentional capability that scientific theories seem not to possess. This distinction is associated with two types of program verification, which we call empirical and intentional verification. We demonstrate, by this means, that computational phenomena are also intentional phenomena, and that such is particularly manifest in agent-based social simulation. Ascertaining the credibility of results in social simulation requires a focus on the identification of a new category of knowledge we can have about computer programs. This knowledge should be considered an outcome of an experimental exercise, albeit not empirical, acquired within a context of limited consensus. The perspective of intentional computation seems to be the only one possible to reflect the multiparadigmatic character of social science in terms of agent-based computational social science. We contribute, additionally, to the clarification of several questions that are found in the methodological perspectives of the discipline, such as the computational nature, the logic of program scalability, and the multiparadigmatic character of agent-based simulation in the social sciences
The logic of the method of agent-based simulation in the social sciences: Empirical and intentional adequacy of computer programs
WOS:000235217900009 (Nº de Acesso Web of Science)The classical theory of computation does not represent an adequate model of reality for simulation in the social sciences. The aim of this paper is to construct a methodological perspective that is able to conciliate the formal and empirical logic of program verification in computer science, with the interpretative and multiparadigmatic logic of the social sciences. We attempt to evaluate whether social simulation implies an additional perspective about the way one can understand the concepts of program and computation. We demonstrate that the logic of social simulation implies at least two distinct types of program verifications that reflect an epistemological distinction in the kind of knowledge one can have about programs. Computer programs seem to possess a causal capability (Fetzer, 1999) and an intentional capability that scientific theories seem not to possess. This distinction is associated with two types of program verification, which we call empirical and intentional verification. We demonstrate, by this means, that computational phenomena are also intentional phenomena, and that such is particularly manifest in agent-based social simulation. Ascertaining the credibility of results in social simulation requires a focus on the identification of a new category of knowledge we can have about computer programs. This knowledge should be considered an outcome of an experimental exercise, albeit not empirical, acquired within a context of limited consensus. The perspective of intentional computation seems to be the only one possible to reflect the multiparadigmatic character of social science in terms of agent-based computational social science. We contribute, additionally, to the clarification of several questions that are found in the methodological perspectives of the discipline, such as the computational nature, the logic of program scalability, and the multiparadigmatic character of agent-based simulation in the social sciences
The Semantic Web Paradigm for a Real-Time Agent Control (Part I)
For the Semantic Web point of view, computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning. Adding logic to the Web, the means to use rules to make inferences, choose courses of action and answer questions, is the actual task for the distributed IT community. The real power of Intelligent Web will be realized when people create many programs that collect Web content from diverse sources, process the information and exchange the results with other programs. The first part of this paper is an introductory of Semantic Web properties, and summarises agent characteristics and their actual importance in digital economy. The second part presents the predictability of a multiagent system used in a learning process for a control problem.Semantic Web, agents, fuzzy knowledge, evolutionary computing
- …