Case handling is a new paradigm for supporting flexible and knowledge intensive business processes. It is strongly based on data as the typical product of these processes.

Agent-based computing represents an exciting new synthesis both for Artificial Intelligence (AI) and, more generally, Computer Science. It has the potential to significantly improve the theory and the practice of modelling, designing, and implementing complex systems. Yet, to date, there has been little systematic analysis of what makes an agent such an appealing and powerful conceptual model. Moreover, even less effort has been devoted to exploring the inherent disadvantages that stem from adopting an agent-oriented view. Here both sets of issues are explored. The standpoint of this analysis is the role of agent-based software in solving complex, real-world problems. In particular, it will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, highlevel social interactions, and that can operate within flexible organisational structures. 1

: The claim that interactive systems have richer behavior than algorithms is surprisingly easy to prove: Turing machines cannot model interaction machines because: interaction is not expressible by a finite initial input string. Interaction machines extend the Chomsky hierarchy, are modeled by interaction grammars, and precisely capture fuzzy concepts like open systems and empirical computer science. Part I of this paper examines extensions to interactive models for algorithms, machines, grammars, and semantics, while part II considers the expressiveness of different forms of interaction. Interactive identity machines are already more powerful than Turing machines, while noninteractive parallelism and distribution are algorithmic. The extension of Turing to interaction machines parallels that of the lambda to the pi calculus, but the ability to model shared state allows interaction machines to express more powerful behavior than calculi. Asynchronous and nonserializable interaction ar...

In this paper we describe and analyze models of problem solving and computation going beyond Turing Machines. Three principles of extending the Turing Machine's expressiveness are identified, namely, by interaction, evolution and infinity. Several models utilizing the above principles are presented. Other

This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor --- a sequence of steps --- with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think.

this paper, this s ection wills how the impact of a coordination model on the des ign and implementation of a multiagents ys tem, by dis cus s ing the cas es tudy of a multiagents ys tem in charge of managing the review proces s for an international conference [2]. Firs t, we will briefly compare two di#erent approaches to the problem, according to a data-driven [12] and a control-driven [13] model. Then, we will dis cus s an hybrid approach, exploiting the TuCSoN coordination model for multiagents ys tems [10]. As a res ult, wes hould be able to point outs everal relevant cons equences of adopting a coordination viewpoint on the engineering of a multiagents ys tem, which are all bas ically related to thes eparation of concerns between the computation and the coordination is s ues , or, in other terms , between individual and s cial ta s s .des gn focus -- agent interaction protocols can be des gned by focusO ng on its individual tas k(s ) (e.g., concentrating on the information needed and produced/inferred by the agent in the proces s of achieving its tas ks ), in manys ens e dis regarding thes ocial tas k(s ) of the groups the agent belongs to;

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. Persistent Turing Machines (PTMs) are multitape machines with a persistent worktape preserved between interactions, whose inputs and outputs are dynamically generated streams of tokens (strings). They are a minimal extension of Turing Machines (TMs) that express interactive behavior. They provide a natural model for sequential interactive computation such as single-user databases and intelligent agents. PTM behavior is characterized observationally, by input-output streams; the notions of equivalence and expressiveness for PTMs are defined relative to its behavior. Four different models of PTM behavior are examined: language-based, automaton-based, function-based, and environment-based. A number of special subclasses of PTMs are identified; several expressiveness results are obtained, both for the general class of all PTMs and for the special subclasses, proving the conjecture in [We2] that interactive computing devices are more expressive than TMs. The methods and tool...

We present Persistent Turing Machines (PTMs), a new way of interpreting Turing-machine computation, one that is both interactive and persistent. A PTM repeatedly receives an input token from the environment, computes for a while, and then outputs the result. Moreover, it can \remember" its previous state (work-tape contents) upon commencing a new computation. We show that the class of PTMs is isomorphic to a very general class of eective transition systems, thereby allowing one to view PTMs as transition systems \in disguise." The persistent stream language (PSL) of a PTM is a coinductively dened set of interaction streams : innite sequences of pairs of the form (w i ; w o ), recording, for each interaction with the environment, the input token received by the PTM and the corresponding output token. We dene an innite hierarchy of successively ner equivalences for PTMs over nite interaction-stream prexes and show that the limit of this hierarchy does not coincide with PSL-equivalence. The presence of this \gap" can be attributed to the fact that the transition systems corresponding to PTM computations naturally exhibit unbounded nondeterminism. We also consider amnesic PTMs, where each new computation begins with a blank work tape, and a corresponding notion of equivalence based on amnesic stream languages (ASLs). We show that the class of ASLs is strictly contained in the class of PSLs. Amnesic stream languages are representative of the classical view of Turing-machine computation. One may consequently conclude that, in a stream-based setting, the extension of the Turing-machine model with persistence is a nontrivial one, and provides a formal foundation for reasoning about programming concepts such as objects with static elds. We additional...

The irreducibility of interactive to algorithmic computing requires fundamental questions concerning models of computation to be reexamined. This paper reviews single-stream and multiple-stream interaction machines, extensions of set theory and algebra for models of sequential interaction, and interactive extensions of the Turing test. It motivates the use of interactive models as a basis for applications to computer architecture, software engineering, and artificial intelligence.