Sycara (1998) Multiagent Systems

I am reading this paper as part of trying to put together a long overdue proceedings for the Agents and Peer to Peer Computing (AP2PC) workshop that was held in Hawaii in 2007. This is a classic paper presenting key ideas in Multi-agent systems (MAS) and research work that had addressed them. The paper is cited by 588 [ATGSATOP]. The first thing that strikes me in the abstract of the paper is that the author says:

the need for systems that consist of multiple agents that communicate in a peer-to-peer fashion is becoming apparent

What's striking is the use of the term peer-to-peer, before Napster, Gnutella and similar systems were released, spawning the field of P2P research. This paper reminds me that agent research has a strong AI theme and Sycara talks about how given particularly difficult problem domains, the only solution is:

to develop a number of functionally specific and (nearly) modular components (agents) that are specialized at solving a particular problem aspect. This decomposition allows each agent to use the most appropriate paradigm for solving its particular problem. When interdependent problems arise, the agents in the system must coordinate with one another to ensure that interdependencies are properly managed.

Which makes me think that the key point here is about systems having loosely linked independent modules. Sycara says that:

Currently, agents on the internet mostly perform information retrieval and filtering. The next generation of agent technology will perform information gathering in context and sophisticated reasoning in support of user problem-solving tasks.

And it seems to me that this next generation of agent technology has not come to be widely used by those outside academia. I think this is the key theme of Hendlers article ...

Sycara describes the characteristics of MASs as follows:

1. each agent has incomplete information of capabilities for solving a problem and thus a limited viewpoint
2. there is no system global control
3. data are decentralized
4. computation is asynchronous

Interest in MAS research is motivated by

1. Solving problems too large for a single agent (resource limitations, avoiding bottlenecks)
2. Supporting interconnection of legacy systems (agent wrappers)
3. Providing solutions to problems that naturally decompose into interacting agents, e.g. calendar scheduling
4. Providing solutions to the efficiently use spatially distributed information sources, e.g. in sensor networks
5. Providing solutions in situations where expertise is distributed, e.g. healthcare
6. Improving performance through concurrency, reliability, extensibility, maintainability, responsiveness, flexibility, reuse

This last point about improving maintainability and reuse makes me think about the field of software maintenance that Philip Johnson mentioned to me. I don't know what extent there has been a dialogue between the two fields, but it feels a little as if the promise of maintainability and reuse in multi-agent systems was made in something of a vacuum. It feels like there is an issue of maintainability and reuse in academic software, which is different from that of industrial software. Or maybe it's not so different, but maybe the problem of getting a reliable behaviour out of a system of agents was much harder than imagined. I mean it sounds great in principle, upgrading the system is just a question of inserting new agents, but knowing how an injection of new sorts of agents will effect the overall system dynamic seems extremely challenging. From my perspective it seems that developments in test driven development (TDD) and behaviour driven development (BDD) are simpler steps that have been more directly addressing the core problem of maintainability and reuse.

Sycara lists what she sees as the key challenges facing MAS in 1998 (would be very interesting to find out what subset she thinks remain 10 years later):

1. How to formulate, describe, decompose and allocate problems and synthesize results among a group of agents
2. How to enable communication and interaction between agents, and allow them to find one another in an open environment
3. How to ensure coherent actions and decision making, and avoid unstable behaviour
4. How should agents represent and reason about the states of other agents
5. How to recognize and resolve conflicts between agents
6. How to engineer and constrain practical distributed AI systems

Sycara then goes on to look at a number of topics in detail. Individual agent reasoning is seen as deliberative, reactive or hybrid. Agent organizations can be hierarchical, expert communities, market-based or scientific communities. The contract net protocol (CNP) is cited as an example of a solution to the task allocation problem; the challenge of dividing up a set of tasks between multiple agents so as to minimize conflicts and communication overhead.

Multiagent planning apparently often relies on a single sophisticated agent to do complex reasoning, although the functionally accurate model (F/AC) uses techniques such as partial global planning (PGP) to get away from this. Other approaches such as joint-intentions framework (all agents must agree shared beliefs and commitments) and SharedPlan (agents have intentions that prompt action or communication) have improved? on earlier work, while STEAM is a hybrid approach that combines joint intentions with SharedPlan.

Recognizing and resolving conflicts seems to be a core area for Sycara, main approach is negotiation but alternatives include backing up propositions to their assumptions, evidential reasoning and argumentation, constraint relaxation and social norms. Regarding negotiation Sycara refers to her PERSUADER system and the game theoretical work of Rosenschein, but I find it really difficult to imagine artificial agents handling negotiation in anything other than an inflexible and trivial fashion. However I have Sycara's PERSUADER paper and another by Rosenchein queued up and I should be reading those next.

Sycara considers other areas such as modelling other agents, managing communication and resources, and adaptation/learning. It always seems to me that modelling other agents would create some sort of complexity feedback loop. Managing communication and resources seems to be an area of huge overlap between peer to peer and agents. Sycara's distributed constraint heuristic search (Sycara et al., 1991) seems like an important forerunner of developments in the peer to peer field. In the section on adaptation and learning Sycara concedes that:

Enhancing the decision-making abilities of some of the individuals in the system can either improve or severely degrade overall system performance.

And this is apparently because complex behaviour can be exhibited by even simple computational eco-systems; thus the addition of learning/adaptivity can have a variable effect, and I would have thought the same applies to giving agents the ability to model each others beliefs.

In a section on applications Sycara lists multiple industrial applications such as multiple vehicle monitoring (Durfee, 1996), manufacturing systems (Parunak, 1987), monitoring and diagnosing faults in nuclear power plants (Wang & Wang, 1997), spacecraft control (Schwuttke & Quan, 1993), climate control (Huberman & Clearwater, 1995), air traffic control (Ljunberg & Lucas, 1992), management of financial portfolios (Sycara et al., 1996) and telecommunications (Weihmayer & Velthuisjen, 1994).

In conclusion Sycara says that:

Designing and building agent systems is difficult. They have all the problems associated with building traditional distributed, concurrent systems and have the additional difficulties that arise from having flexible and sophisticated interactions between autonomous problem-solving components. The big question then becomes one of how effective MASs can be designed and implemented.

She describes three key issues as impeding the widespread adoption of multiagent technology:

1. Lack of a systematic design methodology
2. Lack of widely available industrial strength MAS toolkits
3. Lack of comfort with the idea of delegating responsibility to autonomous agents

I would be very interested to see what Sycara's current perspective on the field is, although I think there is general agreement that multiagent systems have not so far achieved widespread adoption, and in fact how to address that problem appears to be the theme of a recent issue of the Journal of Agent Oriented Software Engineering and the introduction to that issue concludes that industrial adoption will be amplified if the following steps are taken:

1. Better communication with industry. Focusing on incremental introduction of agent concepts, and in particular, building on conventional methods and terminologies, and translating concepts and techniques from multi-agent systems into conventional software engineering practice.
2. Improve the connection with the conventional software engineering community. Agent researcher could present their work in conventional software engineering venues, and participate actively in related research initiatives.
3. Broaden the research agenda. In particular:

• Focus on goal-oriented design. Goals are what make agents adaptable, and adaptability is a major concern of today software systems.
• Enhance research on architectural patterns and styles. Patterns embody know-how in an established form and allow architects to adopt a particular multi-agent system approach.
• Extend research on validation and verification. Guarantees about the stakeholder requirements is a prerequisite for industrial adoption of multi-agent systems.

What I take away from all this is that while I find it difficult to imagine how agent programming or multi-agent systems could be of much help anywhere, I am often thinking of web application systems or other strongly user-oriented systems, when of course there are huge amounts of programming going on to create software to control chemical plants, supervise manufacturing and so on. It does seem plausible to me that an agent-based methodology can help in those environments, however when I reflect on it I realise that I am really not so interested in industrial software. I am really interested in the human computer interface, and it is there in general, and in web applications in specific, that multi-agent systems don't have much to offer. In some ways it seems like the agent hype getting all mixed up in the web hype was not such a good thing. Of course interface design is an area where the concept of agency and trust is very important, but theoretical results about the stability of populations of interacting agents don't shed much insight into human computer interface design, and I think all the years I was researching agents and peer to peer, what I was really interested in was interfaces. It's taken me a long time to work that out :-)

I guess the other thing I take away is how the huge swathes of DAI/Multiagent research had relatively little influence on the developers hacking up the first P2P systems and that was quite difficult for academic researchers to bear. At least that is my perception. I think this often happens - for academics who are aware of whole fields of research to see people implement things independently from that research, but the bottom line is that it takes a huge amount of effort to become aware of the available literature for a field and even more to infer design guidelines. I think there is an issue here relating to the accessibility of academic research, or perhaps it is just a more general one about older people wanting younger people to gain the benefit of their experience, when the younger people just want to try things out for themselves :-)

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