Notes taken from 'An Introduction to Multiagent Systems' (2002), by Michael Wooldridge
2, Intelligent Agents
An agent is a computer system that is situated in some environment, and that is capable of autonomous action in this environment in order to meet its design objectives.
Environments: Russell and Norvig (1995) suggest the following classification of environment properties:
- Accessible versus inaccessible...
- Deterministic versus non-deterministic...
- Static versus dynamic...
- Discrete versus continuous...
Intelligent Agents: The following list of the kinds of capabilities that we might expect an intelligent agent to have was suggested by Wooldridge and Jennings (1995):
- Reactivity...
- Proactiveness...
- Social ability...
... What turns out to be hard is building a system that achieves an effective balance between goal-directed and reactive behaviour.
(Agents and Objects, Agents and Expert Systems, Agents as Intentional Systems, Abstract Architectures for Intelligent Agents, How to Tell an Agent What to Do, Synthesizing Agents)
Thursday, 21 June 2007
24.1, An Introduction to Multiagent Systems
Notes taken from 'An Introduction to Multiagent Systems' (2002), by Michael Wooldridge
1, Introduction
This book is about multiagent systems. It addresses itself to two key problems:
- How do we build agents that are capable of independent, autonomous action in order to successfully carry out the tasks that we delegate to them?
- How do we build agents that are capable of interacting (cooperating, coordinating, negotiating) with other agents in order to successfully carry out the tasks that we delegate to them, particularly when the other agents cannot be assumed to share the same interests/goals?
The first problem is that of agent design, and the second problem is that of society design. The two problems are not orthogonal - for example, in order to build a society of agents that work together effectively, it may help if we give members of the society models of the other agents in it.
The Vision Thing: "You are in desperate need of a last minute holiday somewhere warm and dry. After specifying your requirements to your personal digital assistant (PDA), it converses with a number of different Web sites, which sell services such as flights, hotel rooms, and hire cars. After hard negotiation on your behalf with a range of sites, your PDA presents you with a package holiday."
There are many basic research problems that need to be solved in order to make such a scenario work; such as:
- How do you state your preferences to your agents?
- How can your agent compare different deals from different vendors?
- What algorithms can your agent use to negotiate with other agents (so as to ensure you are not 'ripped off')?
Objections to Multiagent Systems: Is it not all just distributed/concurrent systems?
In multiagent systems, there are two important twists to the concurrent systems story.
- First, because agents are assumed to be autonomous - capable of making independent decision about what to do in order to satisfy their design objectives - it is generally assumed that synchronization and coordination structures in a multiagent system are not hardwired in at design time, as they typically are in standard concurrent/distributed systems. We therefore need mechanisms that will allow agents to synchronize and coordinate their activities at run time.
- Second, the encounters that occur among computing elements in a multiagent system are economic encounters, in the sense that they are encounters between self-interested entities. In a classic distributed/concurrent system, all the computing elements are implicitly assumed to share a common goal (of making the overall system function correctly). In multiagent systems, it is assumed instead that agents are primarily concerned with their own welfare (although of course they will be acting on behalf of some user/owner).
1, Introduction
This book is about multiagent systems. It addresses itself to two key problems:
- How do we build agents that are capable of independent, autonomous action in order to successfully carry out the tasks that we delegate to them?
- How do we build agents that are capable of interacting (cooperating, coordinating, negotiating) with other agents in order to successfully carry out the tasks that we delegate to them, particularly when the other agents cannot be assumed to share the same interests/goals?
The first problem is that of agent design, and the second problem is that of society design. The two problems are not orthogonal - for example, in order to build a society of agents that work together effectively, it may help if we give members of the society models of the other agents in it.
The Vision Thing: "You are in desperate need of a last minute holiday somewhere warm and dry. After specifying your requirements to your personal digital assistant (PDA), it converses with a number of different Web sites, which sell services such as flights, hotel rooms, and hire cars. After hard negotiation on your behalf with a range of sites, your PDA presents you with a package holiday."
There are many basic research problems that need to be solved in order to make such a scenario work; such as:
- How do you state your preferences to your agents?
- How can your agent compare different deals from different vendors?
- What algorithms can your agent use to negotiate with other agents (so as to ensure you are not 'ripped off')?
Objections to Multiagent Systems: Is it not all just distributed/concurrent systems?
In multiagent systems, there are two important twists to the concurrent systems story.
- First, because agents are assumed to be autonomous - capable of making independent decision about what to do in order to satisfy their design objectives - it is generally assumed that synchronization and coordination structures in a multiagent system are not hardwired in at design time, as they typically are in standard concurrent/distributed systems. We therefore need mechanisms that will allow agents to synchronize and coordinate their activities at run time.
- Second, the encounters that occur among computing elements in a multiagent system are economic encounters, in the sense that they are encounters between self-interested entities. In a classic distributed/concurrent system, all the computing elements are implicitly assumed to share a common goal (of making the overall system function correctly). In multiagent systems, it is assumed instead that agents are primarily concerned with their own welfare (although of course they will be acting on behalf of some user/owner).
Tuesday, 12 June 2007
Backward and Forward Reasoning in Agents
The reasoning core of hybrid agents, which exhibit both rational/deliberative and reactive behaviour, is a proof procedure (executed within an observe-think-act cycle) that combines forward and backward reasoning:
Backward Reasoning: Used primarily for planning, problem solving and other deliberative activities.
Forward Reasoning: Used primarily for reactivity to the environment, possibly including other agents.
Backward Reasoning: Used primarily for planning, problem solving and other deliberative activities.
Forward Reasoning: Used primarily for reactivity to the environment, possibly including other agents.
Conformance to Protocols
A protocol specifies the "rules of encounter" governing a dialogue between agents. It specifies which agent is allowed to say what in a given situation.
There are different levels of (an agent's) conformance to a protocol, as follows:
- Weak conformance - iff it will never utter an illegal dialogue move.
- Exhaustive conformance - iff it is weakly conformant and it will utter at least one dialogue move when required by the protocol.
- Robust conformance - iff it is exhaustively conformant and it utters the (special) dialogue more "not-understood" whenever it receives an illegal move from the other agent.
There are different levels of (an agent's) conformance to a protocol, as follows:
- Weak conformance - iff it will never utter an illegal dialogue move.
- Exhaustive conformance - iff it is weakly conformant and it will utter at least one dialogue move when required by the protocol.
- Robust conformance - iff it is exhaustively conformant and it utters the (special) dialogue more "not-understood" whenever it receives an illegal move from the other agent.
Deduction, Induction, Abduction
Deduction: An analytic process based on the application of the general rules to particular cases, with the inference of a result.
Induction: Synthetic reasoning which infers the rule from the case and the result.
Abduction: Another form of synthetic inference, but of the case from a rule and a result.
Induction: Synthetic reasoning which infers the rule from the case and the result.
Abduction: Another form of synthetic inference, but of the case from a rule and a result.
Friday, 8 June 2007
23, Conflict-free normative agents using assumption-based argumentation
Notes taken from 'Conflict-free normative agents using assumption-based argumentation' (2007), by Dorian Gaertner and Francesca Toni
"... We (map) a form of normative BDI agents onto assumption-based argumentation. By way of this mapping we equip our agents with the capability of resolving conflicts amongst norms, belifs, desires and intentions. This conflict resolution is achieved by using the agent's preferences, represented in a variety of formats..."
1, Introduction
Normative agents that are governed by social norms may see conflicts arise amongst their individual desires, or beliefs, or intentions. These conflicts may be resolved by rendering information (such as norms, beliefs, desires and intentions) defeasible and by enforcing preferences. In turn, argumentation has proved to be a useful technique for reasoning with defeasible information and preferences when conflicts may arise.
In this paper we adopt a model for normative agents, whereby agents hold beliefs, desires and intentions, as in a conventional BDI model, but these mental attitudes are seen as contexts and the relationship amongst them are given by means of bridge rules...
2, BDI+N Agents: Preliminaries
(Background (BDI+N agents), Norm Representation in BDI+N Agents, Example)
3, Conflict Avoidance
(Background (Assumption-based argumentation framework), Naive Translation into Assumption-Based Argumentation, Avoiding Conflicts using Assumption-Based Argumentation)
4, Conflict Resolution using Preferences
(Preferences as a Total Ordering, Preferences as a Partial Ordering, Defining Dynamic Preferences via Meta-rules)
5, Conclusions
In this paper we have proposed to use assumption-based argumentation to solve conflicts that a normative agent can encounter, arising from applying conflicting norms but also due to conflicting beliefs, desires and intentions. We have employed qualitative preferences over an agent's beliefs, desires and intentions and over the norms it is subjected to in order to resolve conflicts...
"... We (map) a form of normative BDI agents onto assumption-based argumentation. By way of this mapping we equip our agents with the capability of resolving conflicts amongst norms, belifs, desires and intentions. This conflict resolution is achieved by using the agent's preferences, represented in a variety of formats..."
1, Introduction
Normative agents that are governed by social norms may see conflicts arise amongst their individual desires, or beliefs, or intentions. These conflicts may be resolved by rendering information (such as norms, beliefs, desires and intentions) defeasible and by enforcing preferences. In turn, argumentation has proved to be a useful technique for reasoning with defeasible information and preferences when conflicts may arise.
In this paper we adopt a model for normative agents, whereby agents hold beliefs, desires and intentions, as in a conventional BDI model, but these mental attitudes are seen as contexts and the relationship amongst them are given by means of bridge rules...
2, BDI+N Agents: Preliminaries
(Background (BDI+N agents), Norm Representation in BDI+N Agents, Example)
3, Conflict Avoidance
(Background (Assumption-based argumentation framework), Naive Translation into Assumption-Based Argumentation, Avoiding Conflicts using Assumption-Based Argumentation)
4, Conflict Resolution using Preferences
(Preferences as a Total Ordering, Preferences as a Partial Ordering, Defining Dynamic Preferences via Meta-rules)
5, Conclusions
In this paper we have proposed to use assumption-based argumentation to solve conflicts that a normative agent can encounter, arising from applying conflicting norms but also due to conflicting beliefs, desires and intentions. We have employed qualitative preferences over an agent's beliefs, desires and intentions and over the norms it is subjected to in order to resolve conflicts...
Tuesday, 5 June 2007
Topics of automated negotiation research
Taken from ‘Automated Negotiation: Prospects, Methods and Challenges’ (2001), by N. R. Jennings et al.
Automated negotiation research can be considered to deal with three broad topics:
- Negotiation Protocols: the set of rules that govern the interaction...
- Negotiation Objects: the range of issues over which agreement must be reached...
- Agents’ Decision Making Models: the decision making apparatus the participants employ to act in line with the negotiation protocol in order to achieve their objectives...
Automated negotiation research can be considered to deal with three broad topics:
- Negotiation Protocols: the set of rules that govern the interaction...
- Negotiation Objects: the range of issues over which agreement must be reached...
- Agents’ Decision Making Models: the decision making apparatus the participants employ to act in line with the negotiation protocol in order to achieve their objectives...
Subscribe to:
Posts (Atom)