Sapient Agents Seven Approaches Zbigniew Skolicki Tomasz Arciszewski Good afternoon, Some day back in April I got a forward of a message, inviting us to write a paper on so-called sapient agents. Since we have already analyzed intelligent agents before, it seemed interesting to see if we can distinguish a subclass of agents, which we could call wise or sapient. The paper which I’m presenting is the outcome of our investigation and we try to see what characteristics sapient agents would have to possess. Let me at this point aknowledge the second author, Professor Tomasz Arciszewski. We work at George Mason University in the School of Information Technology and Engineering.

Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03 Agents vs. Agents A wide spectrum of possibilities Swarm agents vs. complex stand-alone agents Manually designed vs. automatically derived/adjusted/evolved Agents, Intelligent Agents, Sapient Agents As everybody knows there are different kind of programs, systems or devices that researchers call agents. Agents can be tiny and primitive and work in big colonies. Or they can be very complex and work on their own, just interacting with the user. Or they can work in a relatively small group, for example negotiating on some problem. Most of the agents are currently developed manually by researchers or programmers. However they can also be generated automatically, to different degree, as for example in research on evolving teams of robots for playing soccer. Finally we can look closer at how actually agents perform reasoning, how they come to their conclusions and how they create goals. It seems that along this line of reasoning we can differentiate them into just agents, intelligent agents, and sapient agents Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03 An autonomous system situated within an environment, which senses its environment, maintains some knowledge and learns upon obtaining new data and, finally, which acts in pursuit of its own agenda to achieve its goals, possibly influencing the environment Most researches though would probably agree with the following definition: An agent is … I have put several words in bold here. We see that these words are related to the way agents acquires and transforms data and decides about future behavior. This, again, confirms that this aspect of agents is very important and hence defining sapient agents as a new subclass may be beneficial. Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03 So what is the difference between different types of agents? Let us start with an intuitive, high level view. Every agents is situated in some environment. environment Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03 Acting Reasoning And it’s basic activity is to constantly analyze the stimuli, make some reasoning and perform actions. This is for agents in general. Sensing environment Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

Intelligent Agent (IA) Acting Reasoning Now, when we speak about Intelligent Agents – we require something more. We want the reasoning be “intelligent”, which excludes primitive input-output lookup tables. Intelligent agent should also probably be able to adapt to changing environment and learn from data. Sensing environment Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03 Sapient Agent (SA) Acting Reasoning Wisdom Some subclass of Intelligent Agents we are willing to call sapient. Our understanding of the word “sapient” or “wise” lead us to a statement that such agents should have some deep meta-knowledge about the process of reasoning. This knowledge can in some case be called experience or intuition in case if it’s not transparent. We believe that such knowledge is a knowledge about processes in general, understanding that we may reason not having all the information – and, as such, wisdom can sometimes seem illogical and contradicting what seems to be appropriate. Sensing environment Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03 Seven approaches Difficult to give a precise definition at this stage “a definition by coverings” However, there is no agreement at this point to how sapient agents can be defined, so that they constitute a closed subset of agents. What we tried to do is we took several (7) approaches and tried to show what characteristics a sapient agent would have in these approaches. Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

1. Knowledge Representation SA utilizes meta-rules, complex models Heuristic rules SA understands the whole structure of a knowledge graph/ontology The first one is KR approach. If we speak about representing knowledge by terms of rules, we believe that SA should possess some rules about rules, that is meta-rules. A flat model of several thousand rules would not make an agent sapient. It is the hierarchy of different levels of abstraction that would make an agent sapient. Another important issue is that strictly logical rules would not probably be enough. SA needs to have some heuristic rules that are based on observation how the system behaves in a long term. If we were speaking about KR in the form of graph of concepts, a SA should probably be able to first, include abstract concepts as well, as it is in the case of ontologies And second, it should probably understand the whole structure of the graph, by which I mean that it should not only look at links between particular concepts, but also be able to see a higher level of interconnections between subgraphs in the graph. Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03 2. Emergence Understanding at global, macroscopic level Line of Evolution approaches SA recognizes emergent patterns The second approach we called emergence. In emergence occurs when something truly new suddenly appears at the stage, as a result of previous, seemingly unrelated actions. Howevr, emergence is not totally unexpected – if we look at macroscopic level, again we may notice sings of new patterns coming. In the design theory there is a concept of the Line of Evolution – every system evolves along some pattern and at certain level all system go through the same stages (S-curve). Therefore, if the agent could recognize such emergent patterns, we could safely call it sapient Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

3. Exploitation/Exploration SA searches for novel design concepts SA uses several representation spaces? SA focuses on exploration Again, when we speak about design or search , we have two forces acting in opposite directions. Exploitation is responsible for carefully searching a given representation space and is responsible for actually finding a solution. What happens however, if we’re looking in the wrong representation space? It is very beneficial, if we are able to consider several of them, or maybe approach representation space in a constructive way, by extending it to cover new areas. SA would obviously not only master exploitation, but even more, would be able to wisely perform exploration. Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

4. Evolutionary Computation SA makes strategic decisions Diversity maintained Slow convergence Possible short-term diminishment of overall performance A similar situation happens in EC. EC is a very powerful technique (and in fact it’s the main area of my interest ;), but it has problems with finishing in local optima. It is crucial to make strategic decisions, like allowing for local diminishment of fitness, or devoting some computational power just for maintaining sentinels, which help to maintain diversity, which in turn occurs very helpful in dynamical environments. A sapient agent would probably trade speed for safety and assurance. Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03 5.Time SA has wider (long-term) perspective Hierarchical planning Holistic understanding in the case of games SA makes long term decisions This brings us to the time approach. A sapient agent does not necessarily go for obvious short-term goals. It rather looks for global perspective, and seeks the outcome in further future. It tries to avoid blind alleys, by accommodating hierarchical planning, and in the case of games takes decisions based on high-level and possibly abstract understanding of the situation. Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03 6. Domain Dependence SA has inter-domain knowledge Structural adaptation (not just adjusting parameters) SA can abstract knowledge at very high level SA should probably not specialize to any domain. It should still have this inter-domain knowledge, that would help it switch between different domains. Regardless of how abstract such knowledge can be, it creates a frame for underlying, more specific knowledge. SA would probably be able to express judgments, which does not directly come from the domain knowledge it possess. Rather they come from the experience it got from dealing with different domains. Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03 7. Chaos Identification of attractors Multi-solution/stream tracking (as far as possible) SA understands constraints of real world and chooses the safest solution Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

Intelligent Agent (IA) Summary No. Definition Name Intelligent Agent (IA) Sapient Agent (SA) 1 Knowledge Representation Only decision rules A knowledge system 2 Emergence No recognition of emerging patterns Recognition of emerging patterns 3 Exploitation/ Exploration Conducts only exploitation Capable of conducting exploration 4 Evolutionary Computation Classic Evolutionary Algorithm (tactical decisions driven by current population) Algorithm maintaining diversity, long-term benefits in complex and dynamical environments (strategic decisions driven by global understanding) 5 Time Capable of making only short-term decisions Capable of making long-term decisions 6 Domain Dependence Limited adaptive behavior Capable of abstracting knowledge and of adaptive behavior 7 Chaos Unaware of attractors Capable of avoiding or finding attractors Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03 Conclusions First attempt to classify Sapient Agents All approaches separate but interrelated Looking for Big Picture absolutely crucial for SA Good starting point for a discussion Much more work necessary Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03

Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03 Thank you Questions? Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03