A planning approach to the automated synthesis of template-based process models

The design-time specification of flexible processes can be time-consuming and error-prone, due to the high number of tasks involved and their context-dependent nature. Such processes frequently suffer from potential interference among their constituents, since resources are usually shared by the process participants and it is difficult to foresee all the potential tasks interactions in advance. Concurrent tasks may not be independent from each other (e.g., they could operate on the same data at the same time), resulting in incorrect outcomes. To tackle these issues, we propose an approach for the automated synthesis of a library of template-based process models that achieve goals in dynamic and partially specified environments. The approach is based on a declarative problem definition and partial-order planning algorithms for template generation. The resulting templates guarantee sound concurrency in the execution of their activities and are reusable in a variety of partially specified contextual environments. As running example, a disaster response scenario is given. The approach is backed by a formal model and has been tested in experiment

Hierarchical agent supervision

Agent supervision is a form of control/customization where a supervisor restricts the behavior of an agent to enforce certain requirements, while leaving the agent as much autonomy as possible. To facilitate supervision, it is often of interest to consider hierarchical models where a high level abstracts over low-level behavior details. We study hierarchical agent supervision in the context of the situation calculus and the ConGolog agent programming language, where we have a rich first-order representation of the agent state. We define the constraints that ensure that the controllability of in-dividual actions at the high level in fact captures the controllability of their implementation at the low level. On the basis of this, we show that we can obtain the maximally permissive supervisor by first considering only the high-level model and obtaining a high- level supervisor and then refining its actions locally, thus greatly simplifying the supervisor synthesis task

Abstraction of Agents Executing Online and their Abilities in the Situation Calculus

We develop a general framework for abstracting online behavior of an agent that may acquire new knowledge during execution (e.g., by sensing), in the situation calculus and ConGolog. We assume that we have both a high-level action theory and a low-level one that represent the agent's behavior at different levels of detail. In this setting, we define ability to perform a task/achieve a goal, and then show that under some reasonable assumptions, if the agent has a strategy by which she is able to achieve a goal at the high level, then we can refine it into a low-level strategy to do so

Goal Formation through Interaction in the Situation Calculus: A Formal Account Grounded in Behavioral Science

Goal reasoning has been attracting much attention in AI recently. Here, we consider how an agent changes its goals as a result of interaction with humans and peers. In particular, we draw upon a model developed in Behavioral Science, the Elementary Pragmatic Model (EPM). We show how the EPM principles can be incorporated into a sophisticated theory of goal change based on the Situation Calculus. The resulting logical theory supports agents with a wide variety of relational styles, including some that we may consider irrational or creative. This lays the foundations for building autonomous agents that interact with humans in a rich and realistic way, as required by advanced Human-AI collaboration applications

2003, ‘On Deliberation under Incomplete Information and the Inadequacy of entailment and ConsistencyBased Formalizations

Much of the work in agent programming assumes an execution model where an agent has a knowledge base (KB) about the current state of the world, and makes decisions about what to do in terms of what is entailed or consistent with this KB. Deliberation or planning then would involve looking ahead and gauging what would be consistent or entailed at various stages by future KBs. We show that in the presence of sensing, this account of deliberation does not work properly, and propose an alternative that does

Synthesizing a Library of Process Templates through Partial-Order Planning Algorithms

The design time specification of dynamic processes can be time-consuming and error-prone, due to the high number of tasks involved and their context-dependent nature. Such processes frequently suffer from potential interference among their constituents, since resources are usually shared by the process participants and it is difficult to foresee all the potential tasks interactions in advance. Concurrent tasks may not be independent from each other (e.g., they could operate on the same data at the same time), resulting in incorrect outcomes. To address these issues, we propose an approach that exploits partial-order planning algorithms for automatically synthesizing a library of process template definitions for different contextual cases. The resulting templates guarantee sound concurrency in the execution of their activities and are reusable in a variety of partially-known contextual environments

Towards a Goal-oriented Framework for the Automatic Synthesis of Underspecified Activities in Dynamic Processes

It is difficult to produce a detailed model of a dynamic process ahead of time. Such processes may include some underspecified activities whose exact definition is not yet known at design-time, and may not be known until the time that an instance of the process has started execution, due to their context-dependent nature. In this paper, we propose a goal-oriented framework to model and specify dynamic processes that allows us to dynamically select and/or synthesize automatically at run-time the content of underspecified activities

The Nondeterministic Situation Calculus

The standard situation calculus assumes that atomic actions are deterministic. But many domains involve nondeterministic actions, with problems such as fully observable nondeterministic (FOND) planning and high-level program execution requiring solutions. Various approaches have been proposed to accommodate nondeterminism on top of the standard situation calculus language, for instance by introducing nondeterministic programs as in Golog and ConGolog. But a key problem in these approaches is that they don’t clearly distinguish between choices that can be made by the agent and choices that are made by the environment, i.e., angelic vs. devilish nondeterminism. In this paper, we propose a simple extension to the standard situation calculus that accommodates nondeterministic actions and preserves Reiter’s solution to the frame problem and answering projection queries through regression. We also provide a formalization of FOND planning and show how ConGolog high-level program execution in nondeterministic domains can be defined

Modeling Multiagent Systems with CASL - A Feature Interaction Resolution Application

In this paper, we describe the Cognitive Agents Specification Language (CASL), and exhibit its characteristics by using it to model the multiagent feature interaction resolution system described by Griffeth and Velthuijsen [7]. We discuss the main features of CASL that make it a useful language for specifying and verifying multiagent systems. CASL has a nice mix of declarative and procedural elements with a formal semantics to facilitate the verification of properties of CASL specifications

An Embedding of ConGolog in 3APL

Several high-level programming languages for programming agents and robots have been proposed in recent years. Each of these languages has its own features and merits. It is still difficult, however, to compare different programming frameworks and evaluate the relative benefits and disadvantages of these frameworks. In this paper, we present a general method for comparing agent programming frameworks based on a notion of bisimulation, and use it to formally compare the languages ConGolog and 3APL. ConGolog is a concurrent language for high-level robot programming based on the situation calculus. ConGolog provides a logical perspective on robot programming, but also incorporates a number of imperative programming constructs like sequential composition. 3APL is an agent programming language and its semantics offers a more operational perspective on agents. The language is a combination of logic and imperative programming and provides operators for beliefs, goals and plans of an ag..