Miguel Rebollo

La W3C está trabajando en una nueva especificación de OWL que está a punto de aprobarse como estándar.

Todavía no la he mirado con detalle. Dejo para una anotación posterior comentar las diferencias entre las dos, ver qué aporta realmente y si realmente es interesante para lo que estamos haciendo.

Por el momento, puedes acceder a la visión general, a las nuevas características y su sintaxis entre otras cosas.

Y aunque aún esté por aprobar el estándar, ya hay herramientas que permiten razonar con las nuevas especificaciones, como la nueva versión de Pellet.

invited talk by Michael N. Huhns

Interesting metaphores for differente technologies

• DPS: decomposition
• DAI: coordination
• MAS: interaction
• SOC: encapsulated functionality with a public interface

Nowadays, some social challenges in economy, energy, environment, transportation, telecommunication are the great problems of our age. And they’re massive, distributed, many-faced, with a large number of dependant componentes, controlled action is needed, but centralised control infeasible. SO agents are the tool (I guess) for addressing these problems.

Characteristics of agent paradigm:

• large-scale multiagent participation
• spatially distributed
• temporally extended
• uneven progress
• possibly cooperative
• design domain isomorphic to execution domain
• constrained: it can’t pave everything and no semantic mismatches
• solution is not centralised, bat it occurs at the edge

Example: individualised transportation.
routes of rails and traffic are designed centralised by engineers, instead of be done in real.time by passengers. Speeds limits are set centrally andd fixed. Traffic lights are barely reactive to local traffic, when it can be auctioned in each intersection…. and meny other examples.

Example: individualised healthcare
The systems are designed for hospitals and caregivers, but not for patientes

Example: grocewry shopper
supermarket chains use IT to set prices, but they’re no systems for shopper to find fair prices. Even shoppers could use RFID tagged items in their own profit.

Example: governance
a citizen has a vote that is given to a representative to be used for N issues.

Example: energy
Europen S-TEN project is using semantic web tech to make each componente of hte energy grid to report on its status intelligently. The result is a finer grained status to human operators of the grid

Example: taxation
determine the fair share of every one. In general, people doesn’t mind to pay what is fair, but it is diffucult to determine in the case of ‘commons’.

…and a very interesting example in logistics that I’ve prefered to listen to).

All this is compared with the example of Columbia university: they put the building rounded completely by grass. And after one year they just paved the worn paths made by people. This is the same criteria in all the previous examples: let the agents to interact and to create or re-create the model by themselves.

Consensus …
Consensus ontology: a first step towards agreement spaces. Take a look at this.
Consensus behaviour: select a plan/sequence of actions from the behaviour of the rest (emergence?). So you can find the best algorithm to do something

Hyperscale sw development: consensus provides a different way for developing sw: encourage lots of people to contribute to software systems and they use all of their constributions. The problem is how all these contributions can be combined.

Idealised SOC: given the requirements of an applitacion: (i find a sert of services that cover the requirements nad (ii) workflow
but it is still unused. They’re very similar to agents, but agents have some benefits: autonomy, they’re active components, are complex (n-party interactions)…The problem: flexibility and reliability. HOw to fix it?
decrease autonomy increases predictibility n(this is why SOA is more used that agents)
removing semantic inconsistences too
transaction concepts can ensure ACID resutls
agents can recover states and maintain their progress toward overall goals at run-time.

Well, and it ends with a great sentence that I’ll add here. I’ve written this post complelety on-line, so I’ll need to review it and make some (minor) changes.

Well, well, well, finally I’m in a room

Stigmergic Landmark Foraging
Nyree Lemmens, Karl Tuyls

Late

Integrating Organizational Control into Multi-Agent Learning
Chongjie Zhang, Shereif Abdallah, Victor Lesser

Problems of distributed learning

1. 1
2. 2
3. 3

Basic idea: organisational-based supervision framework. It’s a multilevel structure (recursive?) Lowest level network agents are ‘workers’. Each leaning agent reposrt its abstract state to its inmediate supervisor and them use rules a suggestions to transmit its supervisory informatio to its subordinates. Rules are set of forbidden actions and suggestions are actions with a degree in [-1,1], Rules are hard constraints and sugg are soft constraints that represent preferences.

The problem they’ve used to test this model is DTAP (distributed task allocation problem). Using a 27×27 agent grid… only!!!, too small!! I can manage several millions of agents to do the same :-( The results: interesting, but I don’t
understand all this stuff to be used in a small network as this: two supervision levels for such a group of agents.

It scales, but adding more supervision levels that may affect to the performance. I don’t like it. You’ll need a lot of layers for a really big network. Furthermore, in the experiments they’ve used a grid instead of a network and this is not ‘elegant’.

Multiagent Learning in Large Anonymous Games
Ian Kash, Eric Friedman, Joseph Halpern

We need to learn quickly, with minimal information and despite of noise. And to test their method they’re using games, but instead of being game theoretic games, they’re continuous, anonymous and designed games. He explains the method with a simple game but at the end it’s similar to game theory… I hate utility functions for agents. The behaviour can’t be reduced to a number or a function. Agents are more complex that that. We are more complex than that.

A simple algorithm to adapt the agent’s behaviour to the rest, so the dynamic converge despite of having agents making mistakes (so they’re introducing noise) in their decisions. As the number of agents increases, the system is more stable and converges faster… they’ve tried with 100 agents (again, too small for me). This results allows to tolerate strange behaviours.

Learning of Coordination
Francisco Melo, Manuela Veloso

Problem: many MAS solutions assume full joint sate observability because consider only local observability makes the problem too complex to be solved. But in many of these problems agent interactions are local. So they have to learn when interaction/coordination is advantageous. MDP and Q-learning is useed. And to show how it works, with an example of two robots that have to cross a gate.

They introduce a Coordination action (pseudo-action) and agents have to decide when to use this Coord action (it has a small penalty). Interesting method: agents can decide when to coordinate instead of exchange irrelevant messages all the time. They’ve tried with many different scenarios.

Abstraction Pathologies in Extensive Games
Kevin Waugh, Dave Schnizlein, Michael Bowling, Duane Szafron

Talking about poker competition for agents. Just two-playesrs. They use abstractions and the agent has to decice when to refine. Test with no-limit and leduc hold’em (small game, 6 cards deck, one ard ‘hidden’ and the other public). Boring… talking about the details of the game and many, many results.

State-Coupled Replicator Dynamics
Daniel Hennes, Karl Tuyls

Using evolutionary game theory, but it is single state dynamic, so it has to be extended to multi-state. Showing the behaviour in different classic games (as Prisoner’s Dilemma). Definitely…. i’m not interesting on this at all.

Wait a minute, with the examples I’ve seen hat it’s very similiar to our model of agreement, at least how it behaves. I’ll need to take a look to it. Too formal, but I hope that Alberto could help us with this.

Tags: aamas, learning, game theory

invited talk by Klaus G. Troitzsch

Human social systems are among the most complex systems in our world and they share several characteristics with agents. They’re different from physical systems and living systems. He’s talking about common concepts in agency from the viewpoint of human societies and comparing them sometimes with physical systems.

Before using agents, in social sciences many approaches has been used, as econophysics/sociophysics, game theory (OGM, again, I’m becoming hate it), some simulation attempts in the 60s… ups, I’ve just discovered that our model of agreement is sociophysics: agents as particles, with vectorial additivity for their behaviours.

Other interesting thing (related with the small world model I’m trying to find) is how humans take roles. People belong to many groups at the same time and we can not classify this groups in levels, because they co-exists. 

… and many other things as communication, emergence, adaptation or trust.
socially-inspired computing

What MAS can learn from economics and social sciences

• more cooperative and secure agent societies
• create adaptative sw if valid HSS simulators can be created
• trust fromation and negotiation as design patterns for distributed systems engineering

but we’re far sway from creating socially-inspired computing systems.

At the end, too general, nothing really new and a bit boring.

This is my first ‘normal session’ in the AAMAS. Yesterday we had a very boring poster session: three hours standing up just to talk 10 mins. with 4 people :-( And today an stressing morning with 6 parallel sessions Impossible to attend the all the topics I’m interested in.

On the Significance of Synchroneity in Emergent Systems
Adam Campbell, Annie Wu

I’ve arrive late, so I’m only trying to get a chair.

On Recursive Simulation
Latek Maciej, Rob Axtell, Bogumil Kaminski

Using something calles n-th order rationality (I haven’t the foggiest idea about what it is). Oh!, it’s about rationality in games (I don’t like game theory and any utility-based solution). They have a tactical model (that represents the evolution of the environment, without decision making information from other agents). So only the ‘trajectories’ of the policies can be found, nor the policies themselves. A n-th order rationality is defined recursively and… ups, too fast… he’s in the example now :-( playing Blotto (I don’t know this game). He’s talking about cognitive capabilities but, at the end, it’s represented just by one equation. Can knowledge be reduce just to one equation?

Adaptive Learning in Complex Evolving Trade Networks
Tomas Klos, Bart Nooteboom

Motivation: task allocation in networks of trading agents, with input/aoutput relations. IN classical economics, individual nodes are optimized, whereas transaction costr economics is focus on transactions (edges). Agents are rational and opportunistic. Buyers choose make or buy something and it’s implemented using Gale-Shapley algorithm for matching (game theory again… maybe the title of this session is wrong), using preferences based on scores related with potential profit and some trust (loyalty) measure.

Some experimental results, that turn into something interesting when they begin to consider the network itself, at least the indegree and outdegree (4 max. with a random network I guess). So, they have a model to simulate organizations using agents to check hypothesis.

A Mathematical Analysis of Collective Cognitive Convergence
Van Parunak

The idea of CCC is intgeresting: how a ‘closed’ collective can ‘corrupt’ the knowledge being something ‘endemic’. And they’re using agents-based models to explain why this is happening. They have simulated that and now they have a formal model of all this stuff.

An interesting result in theorem 4: it detects when the system converges (non deseable), so it can be corrected. And this convergence depends on number of agents, the number of topics and the topic’s density.

Emergent Service Provisioning and Demand Estimation through Self-Organizing Agent Communities
Mariusz Jacyno, Seth Bullock, Michael Luck, Terry Payne

A simulation model to match supply with demand in service based communities (coalitions and teams). Very difficult to synchronise choices in a centralised way and it leads to over-provisioning of services (againt a reason to create a distributed SF federation and, why not,  using a small world model ;-) This work is based on the emergent behaviour of insect colonies (OMG, ants again!) -> limited knowledge about peers and local behaviour (it sounds to me).

One interesting idea: to change the type of a provided service  has a cost, a penalty, so the system trends to keep the services unchanged as long as possible. Besides, resources are limited so a limited-size registry of known providers are maintained by customers, whereas providers have a limited-seize registry of user’s requests.

The simulation considers the amount of memory and only two types of services are available. When you have too few or too much memory the performance is worse that in a medium case, where you know enough providers/customers to work locally without service changes.

Effective Tag Mechanisms for Evolving Cooperation
Matthew Matlock, Sandip Sen

The last one, about considering expertise in a agent network using tag mechanism. Tags are a useful mechanism to promote collaborative behavior and it allows to reuse knowledge

This is my first ‘normal session’ in the AAMAS. Yesterday we had a very boring poster session: three hours standing up just to talk 10 mins. with 4 people :-( And today an stressing morning with 6 parallel sessions Impossible to attend the all the topics I’m interested in.

On the Significance of Synchroneity in Emergent Systems
Adam Campbell, Annie Wu

I’ve arrive late, so I’m only trying to get a chair.

On Recursive Simulation
Latek Maciej, Rob Axtell, Bogumil Kaminski

Using something calles n-th order rationality (I haven’t the foggiest idea about what it is). Oh!, it’s about rationality in games (I don’t like game theory and any utility-based solution). They have a tactical model (that represents the evolution of the environment, without decision making information from other agents). So only the ‘trajectories’ of the policies can be found, nor the policies themselves. A n-th order rationality is defined recursively and… ups, too fast… he’s in the example now :-( playing Blotto (I don’t know this game). He’s talking about cognitive capabilities but, at the end, it’s represented just by one equation. Can knowledge be reduce just to one equation?

Adaptive Learning in Complex Evolving Trade Networks
Tomas Klos, Bart Nooteboom

Motivation: task allocation in networks of trading agents, with input/aoutput relations. IN classical economics, individual nodes are optimized, whereas transaction costr economics is focus on transactions (edges). Agents are rational and opportunistic. Buyers choose make or buy something and it’s implemented using Gale-Shapley algorithm for matching (game theory again… maybe the title of this session is wrong), using preferences based on scores related with potential profit and some trust (loyalty) measure.

Some experimental results, that turn into something interesting when they begin to consider the network itself, at least the indegree and outdegree (4 max. with a random network I guess). So, they have a model to simulate organizations using agents to check hypothesis.

A Mathematical Analysis of Collective Cognitive Convergence
Van Parunak

The idea of CCC is intgeresting: how a ‘closed’ collective can ‘corrupt’ the knowledge being something ‘endemic’. And they’re using agents-based models to explain why this is happening. They have simulated that and now they have a formal model of all this stuff.

An interesting result in theorem 4: it detects when the system converges (non deseable), so it can be corrected. And this convergence depends on number of agents, the number of topics and the topic’s density.

Emergent Service Provisioning and Demand Estimation through Self-Organizing Agent Communities
Mariusz Jacyno, Seth Bullock, Michael Luck, Terry Payne

A simulation model to match supply with demand in service based communities (coalitions and teams). Very difficult to synchronise choices in a centralised way and it leads to over-provisioning of services (againt a reason to create a distributed SF federation and, why not,  using a small world model ;-) This work is based on the emergent behaviour of insect colonies (OMG, ants again!) -> limited knowledge about peers and local behaviour (it sounds to me).

One interesting idea: to change the type of a provided service  has a cost, a penalty, so the system trends to keep the services unchanged as long as possible. Besides, resources are limited so a limited-size registry of known providers are maintained by customers, whereas providers have a limited-seize registry of user’s requests.

The simulation considers the amount of memory and only two types of services are available. When you have too few or too much memory the performance is worse that in a medium case, where you know enough providers/customers to work locally without service changes.

Effective Tag Mechanisms for Evolving Cooperation
Matthew Matlock, Sandip Sen

The last one, about considering expertise in a agent network using tag mechanism. Tags are a useful mechanism to promote collaborative behavior and it allows to reuse knowledge (game theory again). They introduce a boolean function to tag matching and examines four models to extend the tags.

• matched:
• pay off:
• paired reproduction. solves both problems, but they need more research to check if it is robust enough.

(check the paper to complete)

Es el título de mi póster en el AAMAS ’09. Se trata de evaluar si es válido un modelo de red de tipo small world para distribuir a un grupo de plataformas  de agentes en una red de forma que se pueda localizar fácilmente dónde se encuentra un agente con el que nos queremos comunicar.

El modelo de red que se emplea ha sido propuesto por Kleinberg y garantiza que se pouede realizar un proceso de búsqueda voraz (tomando decisiones de forma local y sin volver atrás) acotado. A este tipo de redes se les llama redes navegables.

En un artículo más extenso lo he comparado con otros modelos de redes de tipo small world y la verdad es que sale bastante bien parado: es una red tan buena como la mejor (el modelo de Barabasi según mis pruebas) en cuanto a tolerancia a fallos (destrucción de enlaces en la red), pero mucho mejor en cuanto a la búsqueda, mejorando incluso a los modelos P2P.

Referencia

REBOLLO, M.: Small World Model for Agent Search (Short Paper).- In Proc. of 8th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2009), Decker, Sichman, Sierra and Castelfranchi (eds.), May, 10–15, 2009, Budapest, Hungarytions.