Supporting adaptiveness of cyber-physical processes through action-based formalisms

Cyber Physical Processes (CPPs) refer to a new generation of business processes enacted in many application environments (e.g., emergency management, smart manufacturing, etc.), in which the presence of Internet-of-Things devices and embedded ICT systems (e.g., smartphones, sensors, actuators) strongly influences the coordination of the real-world entities (e.g., humans, robots, etc.) inhabitating such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS, called SmartPM, which combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on three well-established action-based formalisms developed for reasoning about actions in Artificial Intelligence (AI), including the situation calculus, IndiGolog and automated planning. Interestingly, the use of SmartPM does not require any expertise of the internal working of the AI tools involved in the system

Progress in AI Planning Research and Applications

Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning

Cognitive Modeling for Computer Animation: A Comparative Review

Cognitive modeling is a provocative new paradigm that paves the way towards intelligent graphical characters by providing them with logic and reasoning skills. Cognitively empowered self-animating characters will see in the near future a widespread use in the interactive game, multimedia, virtual reality and production animation industries. This review covers three recently-published papers from the field of cognitive modeling for computer animation. The approaches and techniques employed are very different. The cognition model in the first paper is built on top of Soar, which is intended as a general cognitive architecture for developing systems that exhibit intelligent behaviors. The second paper uses an active plan tree and a plan library to achieve the fast and robust reactivity to the environment changes. The third paper, based on an AI formalism known as the situation calculus, develops a cognitive modeling language called CML and uses it to specify a behavior outline or sketch plan to direct the characters in terms of goals. Instead of presenting each paper in isolation then comparatively analyzing them, we take a top-down approach by first classifying the field into three different categories and then attempting to put each paper into a proper category. Hopefully in this way it can provide a more cohesive, systematic view of cognitive modeling approaches employed in computer animation

Reformulation in planning

Reformulation of a problem is intended to make the problem more amenable to efficient solution. This is equally true in the special case of reformulating a planning problem. This paper considers various ways in which reformulation can be exploited in planning

Decision-Theoretic Golog with Qualitative Preferences

Personalization is becoming increasingly important in agent programming, particularly as it relates to the Web. We propose to develop underspecified, task-specific agent programs, and to automatically personalize them to the preferences of individual users. To this end, we propose a framework for agent programming that integrates rich, non-Markovian, qualitative user preferences expressed in a linear temporal logic with quantitative Markovian reward functions. We begin with DTGOLOG, a first-order, decisiontheoretic agent programming language in the situation calculus. We present an algorithm that compiles qualitative preferences into GOLOG programs and prove it sound and complete with respect to the space of solutions. To integrate these preferences into DTGOLOG we introduce the notion of multiprogram synchronization and restate the semantics of the language as a transition semantics. We demonstrate the utility of this framework with an application to personalized travel planning over the Web. To the best of our knowledge this is the first work to combine qualitative and quantitative preferences for agent programming. Further, while the focus of this paper is on the integration of qualitative and quantitative preferences, a side effect of this work is realization of the simpler task of integrating qualitative preferences alone into agent programming as well as the generation of GOLOG programs from LTL formulae.

Multi-agent planning using an abductive : event calculus

Temporal reasoning within distributed Artificial Intelligence Systems is faced with the problem of concurrent streams of action. Well known, logic-based systems using the SITUATION CALCULUS solve the frame problem in a purely linear manner. Recent research, however, has revealed that the EVENT CALCULUS under the abduction principle is capable of nonlinear planning. In this report, we present a planning service module which incorporates this approach into a constraint logic framework and even allows a notion of strong nonlinearity. The work includes the axiomatisation of appropriate versions of the EVENT CALCULUS, the development of a suitably sound and complete proof procedure that supports abduction and the implementation of both of these layers on the constraint platform OZ. We demonstrate prototypically how this module, EVE, can be integrated into an existing multi-agent architecture and evaluate the behaviour of such agents within an application domain, the loading dock scenario

Taming Numbers and Durations in the Model Checking Integrated Planning System

The Model Checking Integrated Planning System (MIPS) is a temporal least commitment heuristic search planner based on a flexible object-oriented workbench architecture. Its design clearly separates explicit and symbolic directed exploration algorithms from the set of on-line and off-line computed estimates and associated data structures. MIPS has shown distinguished performance in the last two international planning competitions. In the last event the description language was extended from pure propositional planning to include numerical state variables, action durations, and plan quality objective functions. Plans were no longer sequences of actions but time-stamped schedules. As a participant of the fully automated track of the competition, MIPS has proven to be a general system; in each track and every benchmark domain it efficiently computed plans of remarkable quality. This article introduces and analyzes the most important algorithmic novelties that were necessary to tackle the new layers of expressiveness in the benchmark problems and to achieve a high level of performance. The extensions include critical path analysis of sequentially generated plans to generate corresponding optimal parallel plans. The linear time algorithm to compute the parallel plan bypasses known NP hardness results for partial ordering by scheduling plans with respect to the set of actions and the imposed precedence relations. The efficiency of this algorithm also allows us to improve the exploration guidance: for each encountered planning state the corresponding approximate sequential plan is scheduled. One major strength of MIPS is its static analysis phase that grounds and simplifies parameterized predicates, functions and operators, that infers knowledge to minimize the state description length, and that detects domain object symmetries. The latter aspect is analyzed in detail. MIPS has been developed to serve as a complete and optimal state space planner, with admissible estimates, exploration engines and branching cuts. In the competition version, however, certain performance compromises had to be made, including floating point arithmetic, weighted heuristic search exploration according to an inadmissible estimate and parameterized optimization

ALPprolog --- A New Logic Programming Method for Dynamic Domains

Logic programming is a powerful paradigm for programming autonomous agents in dynamic domains, as witnessed by languages such as Golog and Flux. In this work we present ALPprolog, an expressive, yet efficient, logic programming language for the online control of agents that have to reason about incomplete information and sensing actions.Comment: 16 page