Koen V. Hindriks

Agent Programming in 3APL

An intriguing and relatively new metaphor in the programming community is that of an intelligent agent. The idea is to view programs as intelligent agents acting on our behalf. By using the metaphor of intelligent agents the programmer views programs as entities which have a mental state consisting of beliefs and goals. The computational behaviour of an agent is explained in terms of the decisions the agent makes on the basis of its mental state. It is assumed that this way of looking at programs may enhance the design and development of complex computational systems.To support this new style of programming, we propose the agent programming language 3APL. 3APL has a clear and formally defined semantics. The operational semantics of the language is defined by means of transition systems. 3APL is a combination of imperative and logic programming. From imperative programming the language inherits the full range of regular programming constructs, including recursive procedures, and a notion of state-based computation. States of agents, however, are belief or knowledge bases, which are different from the usual variable assignments of imperative programming. From logic programming, the language inherits the proof as computation model as a basic means of computation for querying the belief base of an agent. These features are well-understood and provide a solid basis for a structured agent programming language. Moreover, on top of that 3APL agents use so-called practical reasoning rules which extend the familiar recursive rules of imperative programming in several ways. Practical reasoning rules can be used to monitor and revise the goals of an agent, and provide an agent with reflective capabilities.Applying the metaphor of intelligent agents means taking a design stance. From this perspective, a program is taken as an entity with a mental state, which acts pro-actively and reactively, and has reflective capabilities. We illustrate how the metaphor of intelligent agents is supported by the programming language. We also discuss the design of control structures for rule-based agent languages. A control structure provides a solution to the problem of which goals and which rules an agent should select. We provide a concrete and intuitive ordering on the practical reasoning rules on which such a selection mechanism can be based. The ordering is based on the metaphor of intelligent agents. Furthermore, we provide a language with a formal semantics for programming control structures. The main idea is not to integrate this language into the agent language itself, but to provide the facilities for programming control structures at a meta level. The operational semantics is accordingly specified at the meta level, by means of a meta transition system.

Programming Rational Agents in GOAL

The agent programming language GOAL is a high-level programming language to program rational agents that derive their choice of action from their beliefsand goals. The language provides the basic building blocks to design and implementrationalagents by meansofa setofprogramming constructs. These programming constructs allow and facilitate the manipulation of an agent’sbeliefs and goals and to structure its decision-making. GOAL agents are called rational because they satisfy a numberof basic rationality constraints and because they decide to perform actions to further their goals based uponareasoning scheme derived from practical reasoning. The programming concepts of belief and goal incorporated into GOAL provide the basis for this form of reasoning and are similarto their common sense counterparts used everyday to explain the actions that we perform. In addition, GOAL provides the means for agents to focus their attention on specic goals and to communicate at the knowledge level. This provides an intuitive basis for writing high-level agent programs. At the same time these concepts and programming constructs have a well-dened, formal semantics. The formal semantics provides the basis for deninga verication framework for GOAL for verifying and reasoning about GOAL agents whichis similar to some of the wellknownagent logics introduced in the literature.

Agent Programming with Declarative Goals

A long and lasting problem in agent research has been to close the gap between agent logics and agent programming frameworks. The main reason for this problem of establishing a link between agent logics and agent programming frameworks is identified and explained by the fact that agent programming frameworks have not incorporated the concept of a declarative goal. Instead, such frameworks have focused mainly on plans or goals-to-do instead of the end goals to be realised which are also called goals-to-be. In this paper, a new programming language called GOAL is introduced which incorporates such declarative goals. The notion of a commitment strategy - one of the main theoretical insights due to agent logics, which explains the relation between beliefs and goals - is used to construct a computational semantics forGOAL. Finally, a proof theory for proving properties of GOAL agents is introduced. An example program is proven correct by using this programming logic.

GENIUS: AN INTEGRATED ENVIRONMENT FOR SUPPORTING THE DESIGN OF GENERIC AUTOMATED NEGOTIATORS

The design of automated negotiators has been the focus of abundant research in recent years. However, due to difficulties involved in creating generalized agents that can negotiate in several domains and against human counterparts, many automated negotiators are domain specific and their behavior cannot be generalized for other domains. Some of these difficulties arise from the differences inherent within the domains, the need to understand and learn negotiators’ diverse preferences concerning issues of the domain, and the different strategies negotiators can undertake. In this paper we present a system that enables alleviation of the difficulties in the design process of general automated negotiators termed Genius, a General Environment for Negotiation with Intelligent multi‐purpose Usage Simulation. With the constant introduction of new domains, e‐commerce and other applications, which require automated negotiations, generic automated negotiators encompass many benefits and advantages over agents that are designed for a specific domain. Based on experiments conducted with automated agents designed by human subjects using Genius we provide both quantitative and qualitative results to illustrate its efficacy. Finally, we also analyze a recent automated bilateral negotiators competition that was based on Genius. Our results show the advantages and underlying benefits of using Genius and how it can facilitate the design of general automated negotiators.

Computational Modeling of Emotion: Toward Improving the Inter- and Intradisciplinary Exchange

The past years have seen increasing cooperation between psychology and computer science in the field of computational modeling of emotion. However, to realize its potential, the exchange between the two disciplines, as well as the intradisciplinary coordination, should be further improved. We make three proposals for how this could be achieved. The proposals refer to: 1) systematizing and classifying the assumptions of psychological emotion theories; 2) formalizing emotion theories in implementation-independent formal languages (set theory, agent logics); and 3) modeling emotions using general cognitive architectures (such as Soar and ACT-R), general agent architectures (such as the BDI architecture) or general-purpose affective agent architectures. These proposals share two overarching themes. The first is a proposal for modularization: deconstruct emotion theories into basic assumptions; modularize architectures. The second is a proposal for unification and standardization: Translate different emotion theories into a common informal conceptual system or a formal language, or implement them in a common architecture.

A verification framework for agent programming with declarative goals

Abstract A long and lasting problem in agent research has been to close the gap between agent logics and agent programming frameworks. The main reason for this problem of establishing a link between agent logics and agent programming frameworks is identified and explained by the fact that agent programming frameworks have hardly incorporated the concept of a declarative goal. Instead, such frameworks have focused mainly on plans or goals-to-do instead of the end goals to be realised which are also called goals-to-be. In this paper, the programming language GOAL is introduced which incorporates such declarative goals. The notion of a commitment strategy—one of the main theoretical insights due to agent logics, which explains the relation between beliefs and goals—is used to construct a computational semantics for GOAL. Finally, a proof theory for proving properties of GOAL agents is introduced. Thus, the main contribution of this paper, rather than the language GOAL itself, is that we offer a complete theory of agent programming in the sense that our theory provides both for a programming framework and a programming logic for such agents. An example program is proven correct by using this programming logic.

Towards an environment interface standard for agent platforms

We introduce an interface for connecting agent platforms to environments. This interface provides generic functionality for executing actions and for perceiving changes in an agent’s environment. It also provides support for managing an environment, e.g., for starting, pausing and terminating it. Among the benefits of such an interface are (1) standard functionality is provided by the interface implementation itself, and (2) agent platforms that support the interface can connect to any environment that implements the interface. This significantly reduces effort required from agent and environment programmers as the environment code needed to implement the interface needs to be written only once. We propose that the interface presented may be used as a standard that enables agents to control entities in environments. Our starting point for designing such a generic interface is based on a careful study of the various interfaces used by different agent programming languages to connect agent programs to environments. We discuss several case studies that use our interface (an elevator simulator, the well-known agent contest, and an implementation of the interface to connect agents to bots in Unreal Tournament 2004).

Modules as policy-based intentions: modular agent programming in GOAL

Modular programming has the usual benefits associated with structured programming, information hiding and reusability, but also has additional benefits to offer when applied in agent programming. We argue that modules can be viewed as structures similar to that of policy-based intentions [2]. Modules perceived in this way are components within an agent that are triggered in a particular situation and combine the knowledge and skills to adequately pursue the goals of the agent in that situation. The context that triggers the activation of a module defines the interface of the module, which can be specified declaratively, in contrast to the usual functional interpretations of such interfaces. A feature that differentiates our notion of a module from plans is that modules provide an agent with a means to focus its attention on the relevant resources it needs to handle a situation. As a result, modules can be used to control or reduce the underspecification and inherent non-determinism that is typical of agent programs. In the paper, the proposed module concept is incorporated into the agent language GOAL and illustrated by means of a simple example.

Specification and Verification of Multi-agent Systems

Specification and Verification of Multi-agent Systems presents a coherent treatment of the area of formal specification and verification of agent-based systems with a special focus on verification of multi-agent programs. This edited volume includes contributions from international leading researchers in the area, addressing logical formalisms and techniques, such as model checking, theorem proving, and axiomatisations for (semi) automatic verification of agent-based systems. Chapters include: Using Theorem Proving to Verify Properties of Agent Programs The Refinement of Multi-Agent Systems Model Checking Agent Communication Directions for Agent Model Checking Model Checking Logics of Strategic Ability: Complexity Correctness of Mult-Agent Programs: A Hybrid Approach The Norm Implementation Problem in Normative Multi-Agent Systems A Verification Logic for GOAL Agents Using the Maude Term Rewriting Language for Agent Development with Formal Foundations The Cognitive Agents Specification Language and Verification Environment A Temporal Trace Language for Formal Modelling and Analysis of Agent Systemns Assurance of Agent Systems: What Role Should Formal Verification Play? Specification and Verification of Multi-agent Systems is a comprehensive guide that makes a useful tool for researchers, practitioners and students, and serves as a reference work summarizing the state of the art in an accessible manner.

Eliminating interdependencies between issues for multi-issue negotiation

In multi-issue negotiations, issues may be negotiated independently or not. In the latter case, the utility associated with one issue depends on the value of another. These issue dependencies give rise to more complex, non-linear utility spaces. As a consequence, the computational cost and complexity of negotiating interdependent issues is increased significantly compared to the case of independent issues. Several techniques have been proposed to deal with this increased complexity, including, for example, introducing a mediator in the negotiation setting. In this paper, we propose an alternative approach based on a weighted approximation technique to simplify the utility space. We show that given certain natural assumptions about the outcome of negotiation the application of this technique results in an outcome that closely matches with the outcome based on the original, interdependent utility structure. Moreover, using the approximated utility structure, each of the issues can be negotiated independently which ensures that the negotiation is computationally tractable. The approach is illustrated by applying and testing it in a case study.