The agent programming language meta-APL

Abstract. We describe a novel agent programming language, Meta-APL, and give its operational semantics. Meta-APL allows both agent programs and their associated deliberation strategy to be encoded in the same programming language. We define a notion of equivalence between programs written in different agent programming languages based on the notion of weak bisimulation equivalence. We show how to simulate (up to this notion of equivalence) programs written in other agent programming languages by programs of Meta-APL. This involves translating both the agent program and the deliberation strategy under which it is executed into Meta-APL.

Beliefs and Conflicts in a Real World Multiagent System

In a real world multiagent system, where the agents are faced with partial, incomplete and intrinsically dynamic knowledge, conflicts are inevitable. Frequently, different agents have goals or beliefs that cannot hold simultaneously. Conflict resolution methodologies have to be adopted to overcome such undesirable occurrences. In this paper we investigate the application of distributed belief revision techniques as the support for conflict resolution in the analysis of the validity of the candidate beams to be produced in the CERN particle accelerators. This CERN multiagent system contains a higher hierarchy agent, the Specialist agent, which makes use of meta-knowledge (on how the conflicting beliefs have been produced by the other agents) in order to detect which beliefs should be abandoned. Upon solving a conflict, the Specialist instructs the involved agents to revise their beliefs accordingly. Conflicts in the problem domain are mapped into conflicting beliefs of the distributed belief revision system, where they can be handled by proven formal methods. This technique builds on well established concepts and combines them in a new way to solve important problems. We find this approach generally applicable in several domains

The Bayes Linear Programming Language [B/D]

Bayes linear methodology provides a quantitative structure for expressing our beliefs and systematic methods for revising these beliefs given observational data. Particular emphasis is placed upon interpretation of and diagnostics for the specification. The approach is similar in spirit to the standard Bayes analysis, but is constructed so as to avoid much of the burden of specification and computation of the full Bayes case. This report is the first of a series describing Bayes linear methods. In this document, we introduce some of the basic machinery of the theory. Examples, computational issues, detailed derivations of results and approaches to belief elicitation will be addressed in related reports.

The Epistemology of Disagreement: Why Not Bayesianism?

Disagreement is a ubiquitous feature of human life, and philosophers have dutifully attended to it. One important question related to disagreement is epistemological: How does a rational person change her beliefs (if at all) in light of disagreement from others? The typical methodology for answering this question is to endorse a steadfast or conciliatory disagreement norm (and not both) on a priori grounds and selected intuitive cases. In this paper, I argue that this methodology is misguided. Instead, a thoroughgoingly Bayesian strategy is what's needed. Such a strategy provides conciliatory norms in appropriate cases and steadfast norms in appropriate cases. I argue, further, that the few extant efforts to address disagreement in the Bayesian spirit are laudable but uncompelling. A modelling, rather than a functional, approach gets us the right norms and is highly general, allowing the epistemologist to deal with (1) multiple epistemic interlocutors, (2) epistemic superiors and inferiors (i.e. not just epistemic peers), and (3) dependence between interlocutors

Store Format Choice in an Evolving Market . A TPB Approach

<div align=justify>The store choice has been studied extensively in the literature, but store format choice has had limited research attention. The store choice modeling has been primarily done in the random utility theory framework, which however is a neo-economics based view of choice decision that ignores the psychological and behavioral aspects of this planned behavior. The store format choice for bulk grocery purchase despite being a rational context has not been conceptualized in the most accepted construct in attitude behavior, the Theory of Planned Behavior (TPB). Attitude-behavior linkage has been studied extensively in literature but there is still no consensus on the components of attitude, their interrelationship and resultant impact on conation. The Theory of Reasoned Action has evolved over time to incorporate perceived behavioral control and past behavior to improve its explanatory capacity as TPB; however, it has maintained its unidimensionalist approach and has not tested affect and cognition independently for its impact on behavior. It may therefore be relevant to explore the possibility of testing the proposed Converging framework of Affect and Cognition and comment on the relationship of the structural components of attitude and its impact on format choice. The impact of past behavior on future behavior in Theory of Planned Behavior has been ambiguous while there has not been much emphasis on the quality of past experience. The current research takes up the past experience quality and tests it in the attitude behavior relationship as an antecedent of actual behavior. This paper conceptualizes the store format choice behavior in the Theory of Planned Behavior framework by exploring the strength of attitude-behavior relationship mediated through behavioral intention and its impact on format choice as also the independent role of affect and cognition on the format choice.</div>

Cognitive computing meets the internet of things

Abstract: This paper discusses the blend of cognitive computing with the Internet-of-Things that should result into developing cognitive things. Today’s things are confined into a data-supplier role, which deprives them from being the technology of choice for smart applications development. Cognitive computing is about reasoning, learning, explaining, acting, etc. In this paper, cognitive things’ features include functional and non-functional restrictions along with a 3 stage operation cycle that takes into account these restrictions during reasoning, adaptation, and learning. Some implementation details about cognitive things are included in this paper based on a water pipe case-study