How to Do Things With Words

Emotion and Adaptation

An Introduction to MultiAgent Systems.

From the Publisher:
An agent is an entity with domain knowledge, goals and actions. Multi-agent systems are a set of agents which
interact in a common environment. Multi-agent systems deal with the construction of complex systems involving multiple agents and their coordination. A multi-agent system (MAS) is a distributed computing system with
autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.

Introduction to Multiagent Systems

Explanation in artificial intelligence: Insights from the social sciences

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.

Agent Programming in 3APL

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.

Programming Rational Agents in GOAL

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.

/pdf/genius-an-integrated-environment-for-supporting-the-design-2xjt27ubs0.pdf

Genius: an integrated environment for supporting the design of generic automated negotiators

The efficiency of automated multi-issue negotiation depends on the availability and quality of knowledge about an opponent. We present a generic framework based on Bayesian learning to learn an opponent model, i.e. the issue preferences as well as the issue priorities of an opponent. The algorithm proposed is able to effectively learn opponent preferences from bid exchanges by making some assumptions about the preference structure and rationality of the bidding process. The assumptions used are general and consist among others of assumptions about the independency of issue preferences and the topology of functions that are used to model such preferences. Additionally, a rationality assumption is introduced that assumes that agents use a concession-based strategy. It thus extends and generalizes previous work on learning in negotiation by introducing a technique to learn an opponent model for multi-issue negotiations. We present experimental results demonstrating the effectiveness of our approach and discuss an approximation algorithm to ensure scalability of the learning algorithm.

/pdf/opponent-modelling-in-automated-multi-issue-negotiation-5bawmdu75r.pdf

Opponent modelling in automated multi-issue negotiation using Bayesian learning

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.

Koen V. Hindriks

Papers

Agent Programming in 3APL

Programming Rational Agents in GOAL

Genius: an integrated environment for supporting the design of generic automated negotiators

Opponent modelling in automated multi-issue negotiation using Bayesian learning

Agent Programming with Declarative Goals